Precision dialysis monitoring and synchonization system

ABSTRACT

A device, system and related methods for monitoring a mammal with heart failure, kidney disease or both, to make predictions about the likelihood of a life threatening ventricular arrhythmia. The device, system and related methods can have one or more sensors in electronic communication with a processor, the sensors determining one or more physiological parameters of a patient, and communicating the physiological parameter to the processor, and the processors using an algorithm to determine the probability of a ventricular arrhythmia based on the physiological parameters.

FIELD OF THE INVENTION

The device, system and methods of the invention relate to a componentsand methods that can monitor a mammal with heart failure, kidney diseaseor both, and make predictions about the likelihood of a life threateningventricular arrhythmia. The systems and methods of the invention includeelectronic circuits, electronic sensors, a computer processor, analgorithm and a telecommunications set-up. The invention further relatesto methods for signal processing and patient monitoring.

The invention relates to systems and methods for synchronizing andpresenting medical data obtained from sensors to provide precision carefor dialysis patients. The systems and methods can identify dialysissession parameters and provide clinically actionable information thatcan be implemented using special-purpose computers and systems toprovide improved medical outcomes. The systems include implantable andnon-implantable sensors for gathering data on one or more medicalparameters, and for gathering data on one or more dialysis sessionparameter. The collected medical parameter and dialysis sessionparameter data can be presented in synchronized and/or simultaneous formto assist in analysis and possible modification of dialysis parametersto reduce incidence and risk of arrhythmia and/or Sudden Cardiac Death(SCD).

The systems and methods further provide for computer-assisted methodsfor dialysis algorithms capable of implementing clinical action onnetworked health systems in a secure, HIPAA compliant environment basedon any of the collected data. The systems and methods can furtherconnect patient data to an Electronic Medical Record (EMR) providinghistorical data to assist in future dialysis sessions.

BACKGROUND

Dialysis patients have very high mortality rates with Cardiac Diseaseaccounting for 43% of deaths. Data indicates that approximately 27% ofthe mortalities are due to Sudden Cardiac Death (SCD). SCD is unexpecteddeath from a cardiac cause within a short time period, generally lessthan an hour from the onset of symptoms in a person without a priorcondition that would appear to be fatal. In most cases, SCD death occursbecause of Ventricular Arrhythmias (abnormal heart rhythms), includingVentricular Tachycardia (VT) or Ventricular Fibrillation (VF). Moreover,Life Threatening Ventricular Arrhythmia (LTVA), specifically, is one ofthe most common causes of death among Chronic Kidney Disease (CKD)patients. CKD, also known as chronic renal disease, is a progressiveloss in renal function over a period of months or years. Excessivefluid, ions and other toxins accumulate in patients with CKD. AlthoughCKD patients are usually treated by hemodialysis therapy, the treatmentis not continuous, but periodic, causing the build-up of excessiveamount of fluids, electrolytes and waste in the body betweenhemodialysis sessions.

Although hemodialysis and pharmaceutical treatment reduces theconcentration of elevated potassium in the blood and in the tissues in apatient, there is no current method to predict the onset of LTVA. Assuch, there is a need to detect an increase in the likelihood of a LTVA.There is also a need for a method to determine the onset of a lifethreatening cardiac event, so a patient can be prophylactically treatedincluding by the administration of anti-arrhythmic drugs or additionaldialysis. There is also a need for techniques to monitor various bodilyfunctions and parameters such as tissue impedance that can than lead tobetter capabilities to predict LTVA in patients with cardiac disease,and/or in dialysis patients.

Arrhythmias are sometimes caused by the delivery of dialysis. This maybe due to cardiovascular stress caused by fluid overload prior todialysis, rapid changes in fluid or electrolyte levels during dialysis,or the rebalancing of fluid or electrolyte levels in a period shortlyafter dialysis. Hemodialysis patients have a rate of fatal arrhythmiasthat is 40 times greater than the general population. Although End StageRenal Disease (ESRD) patients are at increased risk of arrhythmias andSudden Cardiac Death (SCD), known systems do not relate data for theprevalence or likelihood of arrhythmias to a particular dialysisschedule in an on-going, dynamic, or patient-specific manner. Medicalcare in known dialysis settings fail to provide or respond to feedbackobtained from sensors. Further, it is often unknown what happens after apatient has left the medical facility. Importantly, how a patientresponds to particular course of dialysis is not collected, stored,analyzed, or associated with a patient medical record. Althoughphysicians may collect pertinent data from disparate sources, thecollected data is not obtained nor monitored by specially adapteddialysis computer systems and processors such as implanted ECG andimpedance sensors. Moreover, known systems do not monitor physiologicaldata pre- or post-dialysis. Instead, blood pressure or heart ratemonitoring is limited to 30-minute intervals during treatment.

Known dialysis systems and methods of treatment provide a fixed scheduleof dialysis with little to no monitoring of related medical parameters,and consequently, no adjustment of dialysis parameters. Known dialysissystems also do not collect ECG data when a patient is not inside ahospital setting. Known systems also fail to provide prevalence ofarrhythmias data in relationship to a particular set of dialysisparameters in synchronized form. Instead of providing a precise dialysisschedule that responds to observed arrhythmia events, most dialysis isprescribed in advance with little to no computer assisted monitoring. Assuch, known dialysis systems and methods are incapable of providingpersonalized care keyed to on-going feedback obtained from a patient.

Known dialysis systems further fail to incorporate data obtained fromimplantable medical devices (IMDs), such as implantable dialysisdevices, pacemakers, drug delivery devices, ILRs, blood panels, amicro-fluidics based ambulatory blood composition monitor, or otherdevices that can monitor and record medical information from patients,such as the occurrence of arrhythmia, heart rate, or blood pressure. Assuch, the known systems and methods cannot determine the effect of suchobtained data and thereby cannot improve therapy nor correctly align themonitored data with the periodic occurrences of dialysis for furthercomputation and analysis. Known IMDs are not capable of generating areport tailored to show the relationship between changes in the medicaldata and the occurrence of dialysis for a specific patient. Knownsystems also cannot monitor at-home dialysis systems. Known monitoringsystems do not provide a method to generate reports showing arelationship for long periods before and after dialysis for at-homesystems.

Hence, there is a need for a personalized dialysis system that canpresent and analyze data to optimize dialysis treatment. There is also aneed for obtaining, monitoring, and presenting patient data in anon-going, dynamic, or patient-specific manner. The need extends toproviding a display for showing a relationship between data from atleast one dialysis session parameter and data from at least one medicalparameter. The need extends to systems and methods configured formonitoring patients when they are not under direct observation in ahospital or medical care setting. The need includes collecting,analyzing, and displaying inter-session data between dialysis visits.The need further includes configurations for monitoring and transmittingsuch data for at-home patients.

There is also a need for a medical monitoring system that cansimultaneously provide the monitored medical data and show theoccurrence of dialysis sessions so that health care personnel canproperly interpret the changes in patient health due to dialysis,thereby enabling changes in dialysis treatment in order to avoidunwanted medical issues. There is further a need for a medicalmonitoring system that can provide displayed reports configured to theneeds of medical professionals or researchers for interpreting theeffects of dialysis on medical parameters.

There is a need for monitoring physiological data pre- or post-dialysis.There is further a need for providing a precise dialysis schedule thatresponds to observed arrhythmia events using specially adapted computersand dialysis systems suitable for the requirements of a healthcaresetting including patient privacy. There is a need for dialysis systemsand methods capable of providing personalized care based on dataobtained from a patient. The dialysis systems and methods shouldincorporate data obtained from implantable medical devices (IMDs), suchas implantable dialysis devices, pacemakers, drug delivery devices,ILRs, blood panels, a micro-fluidics based ambulatory blood compositionmonitor, or other devices that can monitor and record medicalinformation from patients, such as the occurrence of arrhythmia, heartrate, or blood pressure. The need extends to correctly aligning therapyparameters based on the monitored data using specially adapted computersthat address the technical challenges extant in healthcare settings suchas patient safety and privacy.

SUMMARY OF THE INVENTION

The first aspect of the invention is directed to a medical device andits accompanying algorithm for monitoring of subjects with cardiacdisease or kidney disease receiving dialysis treatment, and relatedmedical systems, methods for providing improved treatment and diagnosticmedical devices and related monitoring algorithms.

In any embodiment of the first aspect of the invention, a medical devicecan comprise one or more sensors in electronic communication with amedical device processor, the sensors determining one or morephysiological parameters, and communicating the physiological parameterto the medical device processor, and the medical device processor usingan algorithm to determine the probability of a ventricular arrhythmiabased on the physiological parameters.

In any embodiment of the first aspect of the invention, thephysiological parameters can be any one selected from the group oftissue impedance, the number of spontaneous ventricular tachycardia andventricular fibrillation events per day, the time spent in atrialfibrillation, and information about the patient's dialysis status data.In any embodiment of the first aspect of the invention, at least one ofthe sensors can be a component integrated into an implantable medicaldevice. In any embodiment of the first aspect of the invention, themedical device can have a signaling mechanism to signal if theprobability of ventricular arrhythmia is higher than a preset value. Inany embodiment aspect of the invention, the processor can be implantedinto a patient.

In any embodiment of the first aspect of the invention, the algorithm,for each of the one or more physiological parameters, can calculate anindividual hazard estimate given by y₁(t)=h₁(t){circle around (×)}x₁(t),wherein h₁(t)=k₁e^(k) ² ^(t), t is time, y₁(t) is the individual hazardestimate at time t, k₁ and k₂ are constants for the given parameter,x₁(t) is the physiological parameter at time t, and {circle around (×)}is a convolution operator.

In any embodiment of the first aspect of the invention, wherein theconvolution operator can be defined by [h₁ {circle around(×)}x₁](n)=Σ_(m=−∞) ^(+∞)h₁(m)x₁(n−m), wherein n is time, h₁(n)=k₁e^(k)² ^(n), x₁(n) is the physiological parameter at time n, and k₁ and k₂are constants for the given parameter.

In any embodiment of the first aspect of the invention, the algorithmcan further calculate a total hazard function: f(t)=Σ_(m=1)^(n)y_(m)(t), wherein f(t) is the total hazard at time t, y_(m)(t) is anindividual hazard estimate for parameter m at time t, and n is thenumber of physiological parameters used.

In any embodiment of the first aspect of the invention, the algorithmcan further calculate a total hazard function:

${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{y_{m}{(t)}}})}}} \right) + k_{9}}$

wherein f(t) is the total hazard at time t, y_(m)(t) is an individualhazard estimate for parameter m at time t, k₉ is an offset coefficient,and n is the number of physiological parameters used.

In any embodiment of the first aspect of the invention, the device canbe configured to allow for entering actual results at time t for thepatient into the medical device processor, and the algorithm can furthercalculate a total error as the difference between the total hazard andthe actual results at time t, and the algorithm can further adjust eachof the coefficients to minimize the error.

In any embodiment of the first aspect of the invention, thephysiological parameters can be any one of selected from the groupconsisting of tissue impedance, number of spontaneous ventriculartachycardia and ventricular fibrillation events per day, time spent inatrial fibrillation, and dialysis status data.

Any of the features disclosed as being part of the first aspect of theinvention can be included in the first aspect of the invention, eitheralone or in combination.

The second aspect of the invention is directed to a method fordetermining the probability of a ventricular arrhythmia. In anyembodiment of the second aspect of the invention, the method can havethe steps of obtaining one or more physiological parameters of a patientfrom one or more sensors, communicating the physiological parameters toa medical device processor, wherein the medical device processorutilizes an algorithm to determine the probability of a ventriculararrhythmia.

In any embodiment of the second aspect of the invention, thephysiological parameters used for the method can be any one of tissueimpedance, the number of spontaneous ventricular tachycardia andventricular fibrillation events per day, time spent in atrialfibrillation, and information about the dialysis status data.

In any embodiment of the second aspect of the invention, at least one ofthe physiological parameters can be obtained from a sensor thatintegrated into an implantable medical device. In any embodiment of thesecond aspect of the invention, the medical device processor can have asignaling mechanism to signal if the probability of ventriculararrhythmia is greater than a pre-set value.

In any embodiment of the second aspect of the invention, the method canalso have a step for treating the patient with a pharmaceutical such asanti-arrhythmia drugs if the probability of ventricular arrhythmia isgreater than a pre-set value.

In any embodiment of the second aspect of the invention, the algorithmin any system, method or device of the invention can calculate for eachof the one or more physiological parameters, an individual hazardestimate given by y₁(t)=h₁(t){circle around (×)}x₁(t), whereinh1(t)=k₁e^(k) ² ^(t), t is time, y1(t) is the individual hazard estimateat time t, k1 and k2 are constants for the given parameter, x1(t) is thephysiological parameter at time t and {circle around (×)} is aconvolution operator.

In any embodiment of the second aspect of the invention, the convolutionoperator in any system, method or device of the invention can be definedby [h₁ {circle around (×)}x₁](n)=Σ_(m=−∞) ^(+∞)h₁(m)x₁(n−m), wherein nis time, h1(n)=k₁e^(k) ² ^(n), x1(n) is the physiological parameter attime n, and k1 and k2 are constants for the given parameter.

In any embodiment of the second aspect of the invention, the algorithmin any system, method or device of the invention can calculate a totalhazard function: f(t)=Σ_(m=1) ^(n)ym(t), wherein f(t) is the totalhazard at time t, ym(t) is an individual hazard estimate for parameter mat time t, and n is the number of physiological parameters used.

In any embodiment of the second aspect of the invention, the algorithmcan calculate a total hazard function:

${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{{ym}{(t)}}})}}} \right) + {k\; 9}}$

wherein f(t) is the total hazard at time t, ym(t) is an individualhazard estimate for parameter m at time t, k9 is an offset coefficient,and n is the number of physiological parameters used.

In any embodiment of the second aspect of the invention, the device,systems or method can also have the steps of entering actual results attime t for the patient into the processor, and the algorithm can alsocalculate a total error as the difference between the total hazard andthe actual results at time t, and the algorithm can further adjust eachof the coefficients to minimize the total error.

In any embodiment of the second aspect of the invention, the algorithmin any system, method or device of the invention can also utilize anadaptive filter to alter each of the coefficients for each patientperiodically. In any embodiment of the second aspect of the invention,the algorithm can utilize the adaptive filter to alter each of thecoefficients for each patient every 14 days.

In any embodiment of the second aspect of the invention, the informationabout the patient's dialysis status data can be determined by assumingthe patient underwent dialysis when the sensor measures a periodic risein tissue impedance. In any embodiment of the second aspect of theinvention, the information about the patient's dialysis status data canbe entered manually.

In any embodiment of the second aspect of the invention, the informationabout the patient's dialysis status data can be communicated to theprocessor from a patient's electronic medical record. In any embodimentsecond aspect of the invention, the method can also have the step ofcommunicating the probability of a ventricular arrhythmia to the patientor a health care professional. In any embodiment of the second aspect ofthe invention, the actual results can be whether or not the patient hasbeen hospitalized.

In any embodiment of the second aspect of the invention, the method canalso have the step of entering actual results at time t for a group ofpatients into the medical device processor, and the algorithm furthercalculates a total error as the difference between the total hazard andthe actual results for the group of patients at time t, and thealgorithm further adjusts each of the coefficients for each patient tominimize the error.

In any embodiment of the second aspect of the invention, a system canhave one or more sensors in electronic communication with a medicaldevice processor, the sensors obtaining one or more physiologicalparameters of a patient and communicating the one or more physiologicalparameters to a medical device processor; and the medical deviceprocessor has an algorithm to determine a probability of a ventriculararrhythmia based on the physiological parameters wherein the processorcan be component of an implantable medical device or be a separatestand-alone unit such as contained in a desktop computer or base module.

Any of the features disclosed as being part of the second aspect of theinvention can be included in the second aspect of the invention, eitheralone or in combination.

The third aspect of the invention is drawn to a use of a device fordetermining the probability of a life-threatening ventriculararrhythmia. In any embodiment of the third aspect of the invention, theuse can comprise the steps of obtaining one or more physiologicalparameters from one or more sensors, communicating the physiologicalparameters to a processor, wherein the processor utilizes an algorithmto determine a probability of a ventricular arrhythmia.

In any embodiment of the third aspect of the invention, thephysiological parameters can be tissue impedance, number of spontaneousventricular tachycardia and ventricular fibrillation events per day,time spent in atrial fibrillation, and information about the dialysisstatus data.

In any embodiment of the third aspect of the invention, at least one ofthe physiological parameters can be obtained from a sensor that is acomponent of an implantable medical device.

In any embodiment of the third aspect of the invention, the processorcan further comprise a signaling mechanism to signal if the probabilityof ventricular arrhythmia is greater than a pre-set value.

In any embodiment of the third aspect of the invention, the algorithm,for each of the one or more physiological parameters, can calculate anindividual hazard estimate given by y₁(t)=h₁(t){circle around (×)}x₁(t),wherein h₁(t)=k₁e^(k) ² ^(t), t is time, y₁(t) is the individual hazardestimate at time t, k₁ and k₂ are constants for the given parameter,x₁(t) is the physiological parameter at time t, and {circle around (×)}is a convolution operator.

In any embodiment of the third aspect of the invention, the convolutionoperator can be defined by [h₁ {circle around (×)}x₁](n)=Σ_(m=−∞)^(+∞)h₁(m)x₁(n−m), wherein n is time, h₁(n)=k₁e^(k) ² ^(n), x₁(n) is thephysiological parameter at time n, and k₁ and k₂ are constants for thegiven parameter.

In any embodiment of the third aspect of the invention, the algorithmcan further calculate a total hazard function: f(t)=Σ_(m=1)^(n)y_(m)(t), wherein f(t) is the total hazard at time t, y_(m)(t) is anindividual hazard estimate for parameter m at time t, and n is thenumber of physiological parameters used.

In any embodiment of the third aspect of the invention, the algorithmcan further calculate a total hazard function:

${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{y_{m}{(t)}}})}}} \right) + k_{9}}$

wherein f(t) is the total hazard at time t, y_(m)(t) is an individualhazard estimate for parameter m at time t, k₉ is an offset coefficient,and n is the number of physiological parameters used.

In any embodiment of the third aspect of the invention, the use canfurther comprise entering actual results at time t for the patient intothe processor, and the algorithm can further calculate a total error asthe difference between the total hazard and the actual results at timet, and the algorithm can further adjust each of the coefficients tominimize the error.

In any embodiment of the third aspect of the invention, the algorithmcan further utilize an adaptive filter to alter the coefficients foreach patient periodically.

In any embodiment of the third aspect of the invention, the algorithmcan utilize the adaptive filter to alter the coefficients for eachpatient every 14 days.

In any embodiment of the third aspect of the invention, the informationabout the dialysis status data can be entered manually.

In any embodiment of the third aspect of the invention, the informationabout the dialysis status data can be communicated to the processor froman electronic medical record.

In any embodiment of the third aspect of the invention, the use canfurther comprise entering actual results at time t for a group ofpatients into the processor, and the algorithm can further calculate atotal error as the difference between the total hazard and the actualresults for the group of patients at time t, and the algorithm canfurther adjust each the coefficients for each patient to minimize theerror.

Any of the features disclosed as being part of the third aspect of theinvention can be included in the third aspect of the invention, eitheralone or in combination.

The fourth aspect of the invention relates to a medical monitoringsystem. In any embodiment of the fourth aspect of the invention, themedical monitoring system can comprise an external or implantablemedical device comprising one or more sensors configured to detect atleast one medical parameter; an input configured to receive at least onedialysis session parameter; a processor in electronic communication withthe external or implantable medical device and the input wherein theprocessor is configured to synchronize the medical parameter to the atleast one dialysis session parameter to determine whether an arrhythmiais related to an occurrence of dialysis.

In any embodiment of the fourth aspect of the invention, the processorcan receive fluid management data and electrolyte management data, anddetermine whether the arrhythmia is due to any one of fluid management,electrolyte management, or both fluid management and electrolytemanagement.

In any embodiment of the fourth aspect of the invention, the system canprovide an electronic-mediated communication to a medical server basedon the determination of whether the arrhythmia is due to any one offluid management, electrolyte management, or both fluid management andelectrolyte management or the system provides an electronic-mediatedcommunication to the medical server to adjust monitoring.

In any embodiment of the fourth aspect of the invention, theelectronic-mediated communication can be electronically transmitted toone or more of a data hub, a handheld data receiver, or a patientelectronic medical record.

In any embodiment of the fourth aspect of the invention, the sensor canbe implanted and electronically transmit data via any one of BluetoothLow Energy, radio frequency, and cellular technologies.

In any embodiment of the fourth aspect of the invention, the medicalparameter can be selected from the group consisting of arrhythmiainformation, heart rate, fluid level, blood ion levels, and bloodpressure, post-dialysis weight, and pre-dialysis weight.

In any embodiment of the fourth aspect of the invention, the dialysissession parameter can be selected from the group consisting of anoccurrence of dialysis, fluid removal rate, dialysate electrolytecomposition, and dialysate pH.

In any embodiment of the fourth aspect of the invention, the system canfurther comprise a non-transitory memory configured to receive and storethe synchronized data from the processor.

Any of the features disclosed as being part of the fourth aspect of theinvention can be included in the fourth aspect of the invention, eitheralone or in combination.

The fifth aspect of the invention is drawn to a medical monitoringsystem that can comprise a medical device comprising one or more sensorsconfigured to sense at least one medical parameter, an input configuredto receive at least one parameter of a dialysis session, a processor inelectronic communication with the medical device and the inputconfigured to obtain the data from the medical device and the input, andto synchronize the data from the medical device and the input; and anoutput configured to output the at least one medical parameter and theat least one parameter of a dialysis session, wherein the output isconfigured to display the at least one medical parameter and at leastone parameter of a dialysis session simultaneously.

In any embodiment of the fifth aspect of the invention, the system canprovide an electronic-mediated communication to a medical server basedon a determination of whether the arrhythmia is due to any one of fluidmanagement, electrolyte management, or both fluid management andelectrolyte management or the processor provides an electronic-mediatedcommunication to a medical server to adjust monitoring.

In any embodiment of the fifth aspect of the invention, the at least onemedical parameter can be selected from the group consisting ofarrhythmia information, heart rate, fluid level, blood ion levels, andblood pressure, post-dialysis weight, and pre-dialysis weight.

In any embodiment of the fifth aspect of the invention, the arrhythmiainformation can comprise at least one parameter selected from the groupconsisting of arrhythmia timing, arrhythmia duration, arrhythmia rate,arrhythmia burden, and arrhythmia type.

In any embodiment of the fifth aspect of the invention, the output cancomprise a chart showing at least one medical parameter and at least onedialysis parameter on the same chart.

In any embodiment of the fifth aspect of the invention, the medicaldevice can be configured to continuously monitor the at least onemedical parameter.

In any embodiment of the fifth aspect of the invention, the medicaldevice can be an implantable medical device.

In any embodiment of the fifth aspect of the invention, the at least oneparameter of a dialysis session can be selected from the groupconsisting of an occurrence of dialysis, dialysis initiation time, adialysis time length, and a dialysis session prescription.

In any embodiment of the fifth aspect of the invention, the output canshow the at least one medical parameter for a preset time before adialysis session, during the dialysis session and for a preset timeafter the dialysis session.

In any embodiment of the fifth aspect of the invention, a user canselect the preset time before the dialysis session and the preset timeafter the dialysis session.

In any embodiment of the fifth aspect of the invention, the presetperiod can be selected from between: 1 hour-1 year, 1-2 hours, 1 hour-1day, 4 hours-7 days, 1 day-1 month, 7 days-30 days, 30 days-6 months, or4 months-1 year.

In any embodiment of the fifth aspect of the invention, the output canshow the at least one medical parameter for a period of time includingmultiple dialysis sessions.

In any embodiment of the fifth aspect of the invention, the input can beconfigured to automatically receive the at least one parameter of adialysis session.

In any embodiment of the fifth aspect of the invention, the input can beconfigured to receive the at least one parameter of a dialysis sessionfrom a dialysis machine or a device in communication with a dialysismachine.

In any embodiment of the fifth aspect of the invention, the medicalmonitoring system can further comprise an interface, wherein theinterface is configured to allow for input of at least one parameter ofa dialysis session.

Any of the features disclosed as being part of the fifth aspect of theinvention can be included in the fifth aspect of the invention, eitheralone or in combination.

The sixth aspect of the invention relates to a method of displayingmedical data and dialysis data simultaneously. In any embodiment of thesixth aspect of the invention, the method can comprise obtaining atleast one set of data of at least one medical parameter from a sensor;obtaining at least one parameter from a dialysis session performed on asubject; and associating the at least one medical parameter and the atleast one dialysis parameter with a time corresponding to the time ofobtaining the at least one medical parameter and at least one dialysisparameter.

In any embodiment of the sixth aspect of the invention, the method cancomprise the step of providing a synchronized output showing the atleast one set of data of at least one medical parameter and the at leastone dialysis parameter as a function of the time.

In any embodiment of the sixth aspect of the invention, the processorcan determine whether an arrhythmia is due to fluid management by one orboth of: comparing a fluid level of the patient to a fluid level of thepatient before, during or after previous dialysis sessions, wherein theprevious dialysis sessions did not result in arrhythmia; and comparingthe rate and magnitude of fluid level drop during a dialysis sessionwithin a set time period of the arrhythmia to a rate and magnitude offluid level drop during a previous dialysis session of the patient,wherein the previous dialysis session did not result in arrhythmia.

In any embodiment of the sixth aspect of the invention, the method canfurther comprise continuously monitoring the at least one medicalparameter for a period of time over multiple dialysis sessions, and theoutput can be a display showing the at least one set of data of at leastone medical parameter for a period of time including multiple dialysissessions.

In any embodiment of the sixth aspect of the invention, the method cancomprise providing an output showing the at least one set of data of atleast one medical parameter for a set time period before the dialysissession, during the dialysis session and after the dialysis session

In any embodiment of the sixth aspect of the invention, the set periodbefore the dialysis session and the set period after a dialysis sessioncan be between any of: 1 hour-1 year, 1-2 hours, 1 hour-1 day, 4 hours-7days, 1 day-1 month, 7 days-30 days, 30 days-6 months, or 4 months-1year.

In any embodiment of the sixth aspect of the invention, the at least oneset of data of at least one medical parameter can be obtained from animplantable medical device.

In any embodiment of the sixth aspect of the invention, at least onedialysis parameter from a dialysis session can be obtained automaticallyfrom a dialysis machine or device in electronic communication with adialysis machine.

In any embodiment of the sixth aspect of the invention, the method cancomprise adjusting a measurement frequency based any one of anoccurrence of arrhythmia, patient fluid level, post-dialysis weight,pre-dialysis weight, or a time duration of an arrhythmia.

In any embodiment of the sixth aspect of the invention, the method cancomprise further comprising obtaining arrhythmia data for a patientduring time periods before, during and after several dialysis sessions,and computing a risk score corresponding to the risk of arrhythmiaduring each of the time periods.

In any embodiment of the f sixth aspect of the invention, the method cancomprise adjusting an ultrafiltration rate or ultrafiltration magnitudebased on the processor determining that a rate or magnitude of fluidlevel drop during a dialysis session within the set time period of thearrhythmia is different from the rate and magnitude of fluid level dropduring the previous dialysis session of the patient.

In any embodiment of the sixth aspect of the invention, the output canbe provided on a monitor.

In any embodiment of the sixth aspect of the invention, the method cancomprise determining the average of the medical parameter value forpre-set time windows before and after each dialysis session, andoutputting the average value of the medical parameter in each windowbefore and after each dialysis session.

In any embodiment of the sixth aspect of the invention, the medicalparameter can comprise at least one of occurrence of an arrhythmia oroccurrence of atrial fibrillation, and the frequency of measurements ofthe medical parameter can be adjusted based on a frequency of theoccurrence of arrhythmia or the occurrence of atrial fibrillation.

In any embodiment of the sixth aspect of the invention, the medicalparameter can comprise at least one of a time duration of atrialfibrillation or a time duration of arrhythmia, and the frequency ofmeasurements of the medical parameter can be adjusted based on the timeduration of atrial fibrillation or the time duration of arrhythmia.

In any embodiment of the sixth aspect of the invention, the medicalparameter can comprise a patient fluid level and an arrhythmiaoccurrence, and the frequency of measurements can be adjusted based onthe patient fluid level.

In any embodiment of the sixth aspect of the invention, the method cancomprise obtaining a fluid level of a patient corresponding to anestimated dry weight of the patient, and the frequency of measurementscan be adjusted based on the difference between the fluid level of thepatient and the fluid level corresponding to the estimated dry weight ofthe patient.

In any embodiment of the sixth aspect of the invention, the method cancomprise obtaining arrhythmia data for a patient during time periodsbefore, during and after several dialysis sessions, and computing a riskscore corresponding to the risk of arrhythmia during each of the timeperiods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the calculation of the total hazardfunction.

FIG. 2 is a block diagram of the predictive model.

FIG. 3 a is an illustration of adaptive version of the predictive model.

FIG. 3 b is a graph showing the error in the prediction as a function ofone of the variables.

FIG. 4 a is a tracing of the daily impedance for a patient in the timedomain.

FIG. 4 b is a tracing of the daily impedance for a patient in thefrequency domain.

FIG. 5 is a histogram showing the day of the week that the minimumtissue impedance is observed in a 1,257 patient data set.

FIG. 6 a is the result of the non-adaptive predictive model when themean values of the coefficients are used.

FIG. 6 b is the result of the non-adaptive predictive model when themedian values of the coefficients are used.

FIG. 6 c is the result of the trivial case where no test is done and nopatient is selected for treatment.

FIG. 7 a is the result of the adaptive predictive model where the meanvalues of the coefficients are used.

FIG. 7 b is the result of the adaptive model when the median values ofthe coefficients are used.

FIG. 8 is a block diagram of the Medtronic Carelink® System.

FIG. 9 is a screenshot of a query using the DWAS system.

FIG. 10 is a diagram of a pacemaker with a bipolar pacing lead.

FIG. 11 is a diagram of a two electrode system for impedancemeasurement.

FIG. 12 is a set up of the system for obtaining real time data during adialysis session.

FIG. 13 is a diagram of a three electrode system for measuringimpedance.

FIG. 14 is a diagram of a four electrode system for measuring impedance.

FIG. 15 is a sample display according to one embodiment showing thearrhythmia data of a patient before, during and after a dialysissession.

FIG. 16 is a sample display according to one embodiment showing thehours of atrial fibrillation per day and the occurrence of dialysis(shaded bars) for the same patient.

FIG. 17 is a sample display according to one embodiment showing theoccurrence of arrhythmia and the occurrence of dialysis for the samepatient.

FIG. 18 is a sample display according to one embodiment showing therelative fluid level of a patient before, during and after a dialysissession.

FIG. 19 shows sample impedance value trends corresponding to changes insubject fluid levels.

FIGS. 20 a-e show sample impedance value trends corresponding to changesin subject fluid levels at a variety of starting impedance values andfluid removal rates.

FIG. 21 is a sample display according to one embodiment showing therelative fluid level of a patient between dialysis sessions.

FIG. 22 is a sample display according to one embodiment showing therelative fluid level of a patient for a time period spanning multipledialysis sessions.

FIG. 23 is a sample display showing the percentage over dry weight of asubject over the course of multiple dialysis sessions.

FIG. 24 is a sample display showing the relative risk of arrhythmia withrespect to the timing of dialysis sessions.

FIG. 25 shows an embodiment of the invention showing the communicationsbetween a dialysis machine, a medical device and a processor.

FIG. 26 shows the inputs and outputs according to an embodiment of theinvention.

FIG. 27 shows a flow chart depicting the traditional method of dealingwith arrhythmia in dialysis patients.

FIG. 28 shows a flow chart depicting a method of dealing witharrhythmias in dialysis patients with the system of the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

Unless defined otherwise, all technical and scientific terms used hereingenerally have the same meaning as commonly understood by one ofordinary skill in the relevant art.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. For example, “an element” means one element or more than oneelement.

The term “actual results” refers to the actual physiological outcomeconcerning a medical parameter for a patient or group of patients.

The term “adaptive filter” refers to a mathematical operation thatrecalculates constants for use in another operation periodically basedon the differences between the predicted results of the second operationand the actual results.

The term “algorithm” refers to a set of steps used to solve amathematical computation.

An “arrhythmia” or “arrhythmia event” is a condition wherein a subject'sheart beats at an abnormal rhythm. As used herein, arrhythmia can referto atrial fibrillation, atrial flutter, ventricular fibrillation,ventricular tachycardia, bradyarrhythmia, or any type of arrhythmiaknown in the art.

“Arrhythmia information” refers to any data corresponding to an observedarrhythmia. Arrhythmia information includes, but is not limited to,arrhythmia timing, arrhythmia type, arrhythmia burden, arrhythmia rate,or arrhythmia duration.

The term “atrial fibrillation” refers to a condition wherein the atriabeat rapidly and irregularly.

“Bluetooth Low Energy” refers to a wireless communication that transmitsdata using ultrahigh frequency radio waves.

“Blood ion levels” as used herein refer to the concentration of specificsolutes in the blood of a patient.

“Cellular technology” refers to a method of transmitting informationelectronically through short wave analog or digital signals to or from atransmitter.

The term “chart,” as used herein describes a diagram, plot, graph, orvisual representation of data of any kind. The chart can be presented ona computer monitor computer window or handheld device and can also bereduced to any tangible medium without limitation.

The term “Chronic Kidney Disease” (CKD) refers to a condition oftencharacterized by the slow loss of kidney function over time. The mostcommon causes of CKD are high blood pressure, diabetes, heart disease,and diseases that cause inflammation in the kidneys. CKD can also becaused by infections or urinary blockages. If CKD progresses, it canlead to end-stage renal disease (ESRD), where the kidneys failcompletely.

The terms “communicate” and “communication” include but are not limitedto, the connection between the electrical elements of the system, eitherdirectly or wirelessly, using optical, electromagnetic, electrical,acoustic or mechanical connections, for data transmission among andbetween said elements.

The term “comprising” includes, but is not limited to, whatever followsthe word “comprising.” Use of the term indicates the listed elements arerequired or mandatory but that other elements are optional and may ormay not be present.

The term “configured to detect” refers to the ability of a sensor ordetector to make a measurement concerning one or more medicalparameters.

The term “configured to receive” refers to the ability of a device toobtain data from another source. The device can be configured to receivedata via electronic transmission of the data or by manual entry of thedata.

The term “consisting of” includes and is limited to whatever follows thephrase the phrase “consisting of.” The phrase indicates the limitedelements are required or mandatory and that no other elements may bepresent.

The phrase “consisting essentially of” includes any elements listedafter the phrase and is limited to other elements that do not interferewith or contribute to the activity or action specified in the disclosurefor the listed elements. Thus, the phrase indicates that the listedelements are required or mandatory but that other elements are optionaland may or may not be present, depending upon whether or not they affectthe activity or action of the listed elements.

A “convolution operator” is a mathematical function that operates on twoother mathematical functions.

“Dialysate electrolyte composition” refers to the types and/orconcentrations of solutes in the dialysate used for a dialysis session.

“Dialysis” is a type of filtration, or a process of selective diffusionthrough a membrane. Dialysis removes solutes of a specific range ofmolecular weights via diffusion through a membrane from a fluid to bedialyzed into a dialysate. During dialysis, a fluid to be dialyzed ispassed over a filter membrane, while dialysate is passed over the otherside of that membrane. Dissolved solutes are transported across thefilter membrane by diffusion between the fluids. The dialysate is usedto remove solutes from the fluid to be dialyzed. The dialysate can alsoprovide enrichment to the other fluid.

A “dialysis parameter,” “dialysis session parameter,” or “parameter of adialysis session” is any factor of a dialysis session that tends toaffect the health of the patient during and after dialysis. As usedherein, “dialysis parameter” includes, but is not limited to, occurrenceof dialysis, fluid removal prescription such as a “dialysis sessionprescription,” fluid removal rate, electrolyte balance of the dialysateor pH of the dialysate.

A “dialysis session” is time period that a patient is treated bydialysis, hemodialysis, hemofiltration, ultrafiltration, or other bloodfluid removal therapy.

A “dialysis session prescription” can refer to any parameter of adialysis session, including the amount of fluid removed, the dialysatecomposition, the rate of fluid removal, the length of the dialysissession, the frequency of dialysis session or any other parameter usedfor one or more dialysis sessions of a patient.

The term “dialysis status,” as used herein, refers to whether thepatient has undergone dialysis, whether and how often the patient isregularly undergoing dialysis, and how long it has been since thepatient's last dialysis session.

The term “display” as used herein means presentation of information on acomputer screen of any type or form.

A “data hub” is a server or computer system designed to aggregate andstore data from multiple sources.

The terms “electrical impedance,” “impedance,” or “tissue impedance,”generally, refer to a measure of the difficulty an electrical currentfaces when it traverses through a biological tissue. Electricalimpedance can be the ratio of the voltage to the current, and given inthe units of Ohms. Electrical impedance can be measured by applying aknown voltage and measuring the electrical current, or by applying aknown electrical current and measuring the resulting voltage. In eithercase, a direct current (DC) or preferably an alternating current (AC)can be used. The AC waveform can be in the form of a sinusoidal current,a square wave, a pulse train or any other repeating form.

The term “electrocardiogram” (ECG) refers to a time varying waveform,produced by the electrical activity of the cardiac muscle and theassociated electrical network within the myocardium. The term is oftenused interchangeably for the ECG tracing available from the surface ofthe subject, or from an implantable device.

“Electrolyte management data” refers to data affecting the electrolytebalance of a patient. Electrolyte management data can include theconcentration of electrolytes in the patient's blood.

“Electronic communication” refers to the connection between theelectrical elements of the system, either directly or wirelessly.

An “electronic-mediated communication” is any communication sent byelectronic means. This includes wireless technology or wiredcommunication.

“Electronic medical records” refers to a digital record of a medicalhistory for a patient.

The term “error” as used herein, refers to the difference between thepredicted results and the actual results for predictions given based onone or more parameters, and include such physiological parameters asdefined herein. “Total error” refers to the combination of all of theerrors for each parameter used in the prediction.

“Fluid management data” refers to data affecting the fluid levels of apatient. The data can include ultrafiltration rate, ultrafiltrationamount, fluid level, pre-dialysis weight, post-dialysis weight, andfrequency of fluid removal.

A “handheld data receiver” refers to a device capable of receiving datain electronic form and small enough to be held in the hand.

The term “implantable medical device” describes a device, component ormodule intended to be totally or partially introduced, surgically ormedically into a mammalian body, or by medical intervention that remainsafter the procedure.

The term “individual hazard estimate” refers to a function thatcorrelates a single variable with a probability of a given eventoccurring.

An “input” as used herein is a component that allows data to be enteredinto or received by a device or system. The input can provision thesubmission of data of any type for further processing by the device orsystem.

An “interface” is a component that allows a user to manually communicateinformation to a processor or a memory device.

The “magnitude of a fluid level drop” refers to the difference betweenthe fluid level of a patient at one point in time and the fluid level ofa patient at some other point in time.

A “medical device” is any device, component or module capable of sensingone or more medical parameters and/or delivering medical therapy.

A “medical device processor” refers a special purpose processor that canhave any one of the following functions of controlling the collection ofexternal or implantable medical device data, controlling the collectionof metadata based on the collected data of any type, synchronizing data,and combinations thereof.

A “medical parameter” is any data that gives information about thehealth of a patient. As used herein, the term “medical parameter”includes, but is not limited to, electromyogram (EMG), anelectroencephalogram (EEG), an electrocardiogram (ECG), tissueimpedance, blood pressure, the level of specific ions in the blood of apatient such as, but not limited to potassium, sodium, and calcium,patient weight including both dry weight and wet weight, pre- andpost-dialysis, a fluid profile including current and historicalprofiles, or other data concerning the health of the patient such asarrhythmia information, heart rate, fluid level, blood ion levels, andblood pressure.

A “medical server” is a specifically designed server capable of storingmedical information. The medical server can have specialized softwareenabling the storage and protection of patient medical information.

“Monitoring” refers to the detection of one or more patient parameters.

The term “offset coefficient” refers to a variable added to an equationin order to offset errors in the equation.

“Occurrence of dialysis” refers to the initiation of a dialysis session,regardless of whether the session is completed as planned.

An “output” as used herein refers to a result obtained from acomputation of any type performed by a processor or computer.

A “patient” is a member of any animal species, preferably a mammalianspecies, optionally a human. The subject can be an apparently healthyindividual, an individual suffering from a disease, or an individualbeing treated for an acute condition or a chronic disease.

A “patient electronic medical record” is a digital file containinghealth or medical information from a patient.

The term “physiological parameter” refers to any data withoutlimitations that gives any medical relevant information about the healthstatus of a patient. As used herein, the term includes, but is notlimited to electromyogram (EMG), an electroencephalogram (EEG), anelectrocardiogram (ECG), tissue impedance, or any other data concerningthe health of the patient. For example, the physiological parameters canencompasses information such as age, weight, gender, current drugtherapies, smoking habits, diet, etc.

“Pre-dialysis weight” refers to the weight of the patient just prior toa dialysis session. “Post-dialysis weight” refers to the weight of apatient immediately after a dialysis session. In any embodiment of thesecond, third, and fourth inventions, the post-dialysis weight can bethe patient's dry weight, or the patient's weight when the patient isnot fluid overloaded. The pre-dialysis weight can be the patient's wetweight, or the weight of the patient while the patient is fluidoverloaded. The pre-dialysis weight minus the post-dialysis weight wouldthen equal the fluid overload of the patient.

The term “pre-set value,” as used herein, refers to a variable whereinthe user can determine the value of the variable.

The term “processor” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart. The term refers without limitation to a computer system, statemachine, processor, or the like designed to perform arithmetic or logicoperations using logic circuitry that responds to and processes thebasic instructions that drive a computer. In any embodiment of thefirst, second, third, and fourth invention, the terms can include ROM(“read-only memory”) and/or RAM (“random-access memory”) associatedtherewith.

The term “programmable” as used herein refers to a device using computerhardware architecture and being capable of carrying out a set ofcommands, automatically.

“Radio frequency” refers to signals in the radio wave portion of theelectromagnetic spectrum.

The term “sensory unit” or “sensor” refers to an electronic componentcapable of measuring a property or condition of interest.

A “signaling mechanism,” as used herein, includes any mechanism capableof alerting a person. The signal may be audible, visual, or through someother means such as vibration, that will get the attention of the user.

The term “simultaneously” as used herein refers to the concurrentpresentation of data or clinical information of any type in any form.The concurrent presentation of data can include data from the sameinstance in time, but is not necessarily limited to particular instancesin time, and can include past data and future expected data.

“Synchronize,” as used herein, means to place two variables of any typeon the same time scale.

The term “total hazard” refers to the total probability of an occurrenceof an event as a function of individual probabilities of an occurrenceof the same event.

The terms “treating” and “treatment” refer to the management and care ofa patient having a pathology or condition. Treating includesadministering one or more embodiments of the present invention toprevent or alleviate the symptoms or complications or to eliminate thedisease, condition, or disorder. As used herein, “treatment” or“therapy” refers to both therapeutic treatment and prophylactic orpreventative measures. “Treating” or “treatment” does not requirecomplete alleviation of signs or symptoms, does not require a cure, andincludes protocols having only a marginal or incomplete effect on apatient.

The term “Ventricular Arrhythmias” refers to any type of abnormal heartrhythms in the ventricles of a heart including Ventricular Tachycardia(VT) or Ventricular Fibrillation (VF) without limitation. One, specific,non-limiting example of a Ventricular Arrhythmia can be a “LifeThreatening Ventricular Arrhythmia” (LTVA).

Medical Device and Algorithm

The first, second, third, fourth, fifth and sixth aspects of theinvention are directed to a medical device and its accompanyingalgorithm for monitoring of subjects with cardiac disease or kidneydisease receiving dialysis treatment, and related medical systems,methods for providing improved treatment and diagnostic medical devicesand related monitoring algorithms.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, a medical device can comprise one or moresensors in electronic communication with a processor, the sensorsdetermining one or more physiological parameters of a patient, andcommunicating the physiological parameter to the processor, and theprocessors using an algorithm to determine the probability of aventricular arrhythmia based on the physiological parameters.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the parameters can be any one selected fromthe group of tissue impedance, the number of spontaneous ventriculartachycardia and ventricular fibrillation events per day, the time spentin atrial fibrillation, and information about the patient's dialysisstatus data. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, at least one of the sensorscan be a component integrated into an implantable medical device. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the medical device can have a signaling mechanism tosignal if the probability of ventricular arrhythmia is higher than apreset value. In another embodiment, the processor can be implanted intothe patient.

The first, second, third, fourth, fifth and sixth aspects of theinvention are also directed to a method for determining the probabilityof a ventricular arrhythmia. In one embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the method canhave the steps of obtaining one or more physiological parameters of apatient from one or more sensors, communicating the parameters to aprocessor, wherein the processor utilizes an algorithm to determine theprobability of a ventricular arrhythmia.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the physiological parameters used for themethod can be any one of tissue impedance, the number of spontaneousventricular tachycardia and ventricular fibrillation events per day,time spent in atrial fibrillation, and information about the dialysisstatus data.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, at least one of the physiological parameterscan be obtained from a sensor that integrated into an implantablemedical device. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the processor can have asignaling mechanism to signal if the probability of ventriculararrhythmia is greater than a pre-set value.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the method can also have a step for treatingthe patient with a pharmaceutical such as anti-arrhythmia drugs if theprobability of ventricular arrhythmia is greater than a pre-set value.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the algorithm in any system, method or deviceof the invention can calculate for each of the one or more physiologicalparameters, an individual hazard estimate given by y₁(t)=h₁(t){circlearound (×)}x₁(t), wherein h1(t)=k₁e^(k) ² ^(t), t is time, y1(t) is theindividual hazard estimate at time t, k1 and k2 are constants for thegiven parameter, x1(t) is the physiological parameter at time t and{circle around (×)} is a convolution operator.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the convolution operator in any system, methodor device of the invention can be defined by [h₁ {circle around(×)}x₁](n)=Σ_(m=−∞) ^(+∞)h₁(m)x₁(n−m), wherein n is time, h1(n)=k₁e^(k)² ^(n), x1(n) is the physiological parameter at time n, and k1 and k2are constants for the given parameter.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the algorithm in any system, method or deviceof the invention can calculate a total hazard function: f(t)=Σ_(m=1)^(n)ym(t), wherein f(t) is the total hazard at time t, ym(t) is anindividual hazard estimate for parameter m at time t, and n is thenumber of physiological parameters used.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the algorithm can calculate a total hazardfunction:

${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{{ym}{(t)}}})}}} \right) + {k\; 9}}$

wherein f(t) is the total hazard at time t, ym(t) is an individualhazard estimate for parameter m at time t, k9 is an offset coefficient,and n is the number of physiological parameters used.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the device, systems or method can also havethe steps of entering actual results at time t for the patient into theprocessor, and the algorithm can also calculate a total error as thedifference between the total hazard and the actual results at time t,and the algorithm can further adjust each of the coefficients tominimize the total error.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the algorithm in any system, method or deviceof the invention can also utilize an adaptive filter to alter each ofthe coefficients for each patient periodically. In another embodiment ofthe first invention, the algorithm can utilize the adaptive filter toalter each of the coefficients for each patient every 14 days.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the information about the patient's dialysisstatus data can be determined by assuming the patient underwent dialysiswhen the sensor measures a periodic rise in tissue impedance. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the information about the patient's dialysis statusdata can be entered manually.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the information about the patient's dialysisstatus data can be communicated to the processor from a patient'selectronic medical record. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the method canalso have the step of communicating the probability of a ventriculararrhythmia to the patient or a health care professional. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the actual results can be whether or not the patienthas been hospitalized.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the method can also have the step of enteringactual results at time t for a group of patients into the processor, andthe algorithm further calculates a total error as the difference betweenthe total hazard and the actual results for the group of patients attime t, and the algorithm further adjusts each of the coefficients foreach patient to minimize the error.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, a system can have one or more sensors inelectronic communication with a processor, the sensors obtaining one ormore physiological parameters of a patient and communicating the one ormore physiological parameters to a processor; and the processor has analgorithm to determine a probability of a ventricular arrhythmia basedon the physiological parameters wherein the processor can be componentof an implantable medical device or be a separate stand-alone unit suchas contained in a desktop computer or base module.

Computing Unit and Algorithm

Unless specifically stated otherwise, as apparent from the foregoingdiscussions, it should be appreciated that throughout the specificationdiscussions utilizing terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices. In asimilar manner, the term “processor” may refer to any device or portionof a device that processes electronic data from registers and/or memoryto transform that electronic data into other electronic data that may bestored in registers and/or memory. A “computing platform” may compriseone or more processors.

Embodiments of the first, second and third aspects of the invention mayinclude apparatuses and/or devices for performing the operations herein.An apparatus may be specially constructed for the desired purposes, orit may comprise a general purpose device selectively activated orreconfigured by a program stored in the device. In any embodiment of thefirst, second and third aspects of the invention, the invention may beimplemented using a combination of any of, e.g., but not limited to,hardware, firmware, and software, etc.

The methods, software and hardware described herein can be embodied inor use transitory or non-transitory computer readable media withinstructions that cause a programmable processor to carry out thetechniques described herein. A “computer-readable medium” includes butis not limited to read-only memory, Flash memory, EPROM and a magneticor optical storage medium. A non-transitory computer readable mediumincludes all computer readable media except for a transitory,propagating signal.

The first, second, third, fourth, fifth and sixth aspects of theinvention can detect an increase in the likelihood of a VentricularArrhythmia, such as LTVA, to provide prophylactic treatment to a patientin need thereof, such as by the administration of a pharmaceutical toalleviate the condition or by hemodialysis or dialysis. In general,functional kidneys remove excess fluids, electrolytes and othermolecules. One of the physiological parameters of kidney function can bemeasured by one or more sensor in the present invention to provide aninput of information into the methods described herein for calculatingthe likelihood of Ventricular Arrhythmia. In particular, a processor orelectronic circuit can use an algorithm to calculate an individualhazard estimate having a physiological parameter obtained from apatient, a time variable, and constants for a given physiologicalparameters.

The hazard function can use any number of physiological parametersunique to the patient. For example, the hazard function can use tissueimpedance, which is directly affected by the amount of fluid beingretained by the patient: as fluid retention increases, the electricalimpedance measured by an implantable device decreases. In one exemplaryalgorithm, a decrease in the electrical impedance can indicate anincrease in fluid retention and an increased propensity toward a LTVA,and can indicate need for dialysis or administration of pharmaceuticalbased on the methods for calculating the likelihood of LTVA describedherein, including but not limited to administration of anti-arrhythmicdrugs. It will be understood that many such parameters and combinationsthereof can be used wherein the present invention can determine a numberof such parameters to be advantageously used.

The first, second, third, fourth, fifth and sixth aspects of theinvention also provide methods for monitoring various bodily functionsthat can than lead to better abilities to predict Ventricular Arrhythmiain patients with cardiac disease, and/or in dialysis patients. Themethods can also reduce the chances for Ventricular Arrhythmia patientssuffer by monitoring and forwarding data for analysis according to themethods described herein.

The processing of the information collected by the sensors is carriedout by a computing unit that can contain a processor or an electroniccircuit. It will be understood that any special purpose machine orcomputer implementing the algorithms contemplated by the presentinvention are encompassed by the broadly intended interpretation of theterm computing unit. In particular, an algorithm processes signals fromall of the sensors to produce an estimation of the risk for anarrhythmia using a mathematical model. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, thealgorithm processes information unique to the patient that is enteredmanually into the computing unit such as patient weight, age, gender,etc. In any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, a non-limiting, and non-exhaustive listof physiological parameters can include AF burden, tissue impedance,ambulatory HRV, respiratory rate, sleep pattern (nocturnal activity),body temperature, heart rate change during dialysis, HRV during thedialysis session, BP reduction during dialysis, mixed venous oxygensaturation, fluid removed during dialysis session, dialysis markersknown to those of ordinary skill in the art such a sodium, potassium,etc. measured periodically, patient weight measured, medications andtheir dosage, and patient supplied data such as (discomfort). Theperformance of the model is monitored by comparing its predictions tothe actual patient outcomes and necessary modifications are made to themodel parameters to improve its performance using the algorithms of thepresent invention.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, four of the input variables, which are dailyimpedance, spontaneous VT/VF, time in AT/AF, and hemodialysis, caninfluence a patient outcome. Further, the effect that any input variablehas on the patient outcome can decrease exponentially as time passes,which will be expressed in the form of a hazard function as shown in Eq.1:

h ₁(t)=k ₁ e ^(k) ² ^(t)

where

h1(t) is the hazard function,

k1 and k2 are constants,

t is time in days.

For each of the input parameters, the hazard function can include twoconstants. For the first input parameter, the constants can be k1 andk2. For the second input parameter, the constants can be k3 and k4, forthe third input parameter the constants can be k5 and k6, for the fourthinput parameter the constants can be k7 and k8, for the fifth inputparameter the constants can be k9 and k10, for the sixth input parameterthe constants can be k11 and k12, for the seventh input parameter theconstants can be k13 and k14, for the eighth input parameter theconstants can be k15 and k16, and for the ninth input parameter theconstants can be k17 and k18. It will be understood that additionalinput parameters can be included having constants kx and ky where xrepresents an iteration of a first constant and y represents aniteration of a second constant.

Individual hazard estimates can be calculated as the convolution of theinput variable and the corresponding hazard function shown in Eq. 2:

y ₁(t)=h ₁(t){circle around (×)}x ₁(t)  [Eq. 2]

where

y1(t) is the individual hazard estimate,

h1(t) is the hazard function

x1(t) is the input function,

{circle around (×)} is the convolution operator.

The convolution can be carried out in the discrete time domain using Eq.3:

y ₁(n)=[h ₁ {circle around (×)}x ₁](n)=Σ_(m=−∞) ^(+∞) h ₁(m)x₁(n−m)  [Eq. 3]

It should be noted that the hazard function h1(t) and the input functionx1(t) representing the data, i.e., physiological parameters, from thepatient have both limited lengths, i.e. they are finite. Hence, thecalculation shown in Eq. 3 can be finite.

The total hazard can be estimated as the summation of the individualhazard estimates as shown in Eq. 4:

y _(T)(t)=f[y ₁(t),y _(Y2)(t),y ₃(t),y ₄(t)]  [Eq. 4]

where

yT(t) is the total hazard estimate,

y1(t), y2(t), y3(t) and y4(t) are the individual hazard estimates, and

f is the summation function.

The summation function, f, may take the form of a linear summation or anon-linear summation, as shown in Eq. 5 and 6.

f _(LIN) [y ₁(t),y ₂(t),y ₃(t),y ₄(t)]=y ₁(t)+y ₂(t)+y ₃(t)+y ₄(t)  [Eq.5]

f _(SIG) [y ₁(t),y ₂(t),y ₃(t),y ₄(t)]=Sigmoidal{y ₁(t)+y ₂(t)+y ₃(t)+y₄(t)+k ₉}  [Eq. 6]

where the Sigmoidal function is given by Eq. 7:

$\begin{matrix}{{{Sigmoidal}(w)} = \frac{1}{1 + ^{- +}}} & \left\lbrack {{Eq}.\mspace{14mu} 7} \right\rbrack\end{matrix}$

The constant k9 shown in Eq. 6 can be added to provide any necessaryoffset for the operation of the Sigmoidal function.

FIG. 1 shows a block diagram for the calculation of the total hazardfunction in any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention. Four input parameters: tissueimpedance 1, ventricular tachycardia and ventricular fibrillation 2,atrial fibrillation 3, and dialysis information 4, can be received bythe computing unit. The computer can calculate the individual hazardestimates according to the equations described herein. The hazardestimates based on tissue impedance 5, ventricular tachycardia andventricular fibrillation 6, atrial fibrillation 7, and dialysisinformation 8 can be processed by the total hazard estimation function9, according to either the linear summation or non-linear summationequations as described herein to generate a total risk estimate. If thenon-linear summation equation is utilized, then the off-set coefficientk9 10 can be added to the total hazard estimation function 9.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, any discrepancy between the estimated hazardand the actual patient outcome can be used for the calculation of theestimation error shown in Eq. 8:

error(n)=z(n)−y _(T)(n)  [Eq. 8]

where

error(n) is the estimation error at time index n,

z(n) is the actual patient outcome at time index n, and

yT(n) is the total hazard estimate at time index n.

Finally, the total error can be calculated as the sum of squared errorsshown in Eq. 11:

$\begin{matrix}{{e_{SS}(n)} = {{\sum\limits_{m = {- \infty}}^{n}{{error}^{2}(m)}} = {\sum\limits_{m = {- \infty}}^{n}\left\lbrack {{z(m)}-=_{T}(m)} \right\rbrack^{2}}}} & \left\lbrack {{Eq}.\mspace{14mu} 11} \right\rbrack\end{matrix}$

When the total error, ess (n), is minimized, the estimator can producean output as close as possible to the actual patient outcome.

FIG. 2 shows the overall block diagram of the predictive model. The fourinput parameters: tissue impedance 21, ventricular tachycardia andventricular fibrillation 22, atrial fibrillation 23 and dialysisinformation 24, are received by the computing unit. The computercalculates the individual hazard estimates according to the equationsdescribed herein. The hazard estimates based on tissue impedance 25,ventricular tachycardia and ventricular fibrillation 26, atrialfibrillation 27 and dialysis information 28 are processed by the totalhazard estimation function 29, according to either the linear summationor non-linear summation equations as described herein to generate atotal risk estimate 30. This total risk estimate can be transmitted tothe user. Additionally, the actual patient outcome 31 is entered, andthe computing unit performs the error determination by the equationsdescribed herein 32 to generate the total error at time t 33. Thecomputer can then use the error determination to correct thecoefficients k1 through k9 34, and feeds the new coefficients back intothe individual hazard functions.

In order to correctly reduce the estimation error, the coefficients kiare to be adjusted. Since there is more than one coefficient, thisprocess requires the utilization of a multi-dimensional optimizationfunction, where the goal is to minimize the total error, ess (n). Manyalgorithms can be used for this task such as Nelder-Mead's downhillsimplex method, which is also known as the Amoeba algorithm. In onepreferred embodiment, Amoeba algorithm is used, because it does notrequire the computation of the derivative of the error, ess (n). [Ref:Numerical Recipes in Fortran, The Art of Scientific Computing, W.H.Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery]. Thedescribed model can be referred to as the non-adaptive model as it doesnot customize itself to each patient or does not adapt when the patientcondition changes. This can be expected because a model using a fixedset of coefficients is not likely to be able account forpatient-to-patient variations. Moreover, a model using a fixed set ofcoefficients cannot adapt to changes that are taking place in thephysiological conditions of a given patient. To address these issues,one advanced version of the model that is contemplated in any embodimentof the first, second, third, fourth, fifth and sixth aspects of theinvention, can be built using an adaptive filter.

As used in any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, an adaptive filter based predictor canbe illustrated in FIG. 3 a and plotted against time 111. The inputparameters: tissue impedance 101, ventricular tachycardia andventricular fibrillation 102, atrial fibrillation 103 and dialysisinformation 104 are fed into the computing unit or processor, which thenperforms the individual hazard calculations and the hazard summationdescribed above 105 to give the total hazard estimate at time t 106. Theactual patient outcome at time t 107 is then entered into the computingunit to calculate the error at time t 108. The error at time t can thenbe entered into the adaptive filter 109 to obtain new values for theconstants k2, k3, k4, k5, k6, k7, k8, k9 110. It will be understood thatthe number of constants k2, k3, k4, k5, k6, k7, k8, k9 are not limitedto nine and range from any number of required constants, including anyone of additional k10 k11, k12, k13, k14 k15, k16, k17, etc. Forinitialization, the model can be started using some initial values forthe parameters k2, k3, k4, k5, k6, k7, k8, k9, which can be determinedusing the non-adaptive model described herein. However, this newadaptive model can then recalculate the error value at each step usingthe data from any number of days, e.g., the last 14 days as shown in Eq.12.

g _(error)(n)=Σ_(m=n-14) ^(n)error²(m)=Σ_(m=n-14) ^(n)[z(m)−=_(T)(m)]²  [Eq. 12]

Once the error, gerror (n), is calculated, its behavior can be studiedas a function of each parameter, k2, k3, k4, k5, k6, k7, k8, k9. Thiscan be accomplished by varying each parameter, one by one, while keepingthe remaining parameters fixed. This can be shown by the curve in FIG. 3b. The graph shows the plot of the total error function gerror (n) 112against the value of k1 113. The parameters k2, k3, k4, k5, k6, k7, k8,k9 are kept constant, while the parameter k1 may vary. One can then plotthe error, gerror, as a function of k1 only. For illustration, assumethat the current value of k1 corresponds to the point labeled as 114 onthe graph. To reduce the error function gerror, k1 must be moved towardthe left, i.e. the value of k1 must be reduced, perhaps by an amount of115. This process is repeated for the remaining parameters, k2, k3, k4,k5, k6, k7, k8, k9, to determine the new values for each parameter,which is used during the next iteration. The adaptive filter can be usedfor the dynamic determination of the coefficients, k2, k3, k4, k5, k6,k7, k8, k9, individually for each subject at each step. In the case offourteen days, the set of representative calculations can be shown byEq.'s 13-16.

h ₁(k ₁ ,k ₂ ,t)=k ₁ e ^(k) ² ^(t)  [Eq. 13]

y ₁(k ₁ ,k ₂ ,t)=h ₁(k ₁ ,k ₂ ,t)

x ₁(t)  [Eq. 14]

Estimate(k ₁ , . . . ,k ₉ ,t)=y _(T)(t)  [Eq. 15]

g _(ERROR)(k ₁ , . . . ,k ₉ ,t)=∫[Actual(τ)−Estimate(τ)]² dτ  [Eq. 16]

One skilled in the art will understand that the adaptive filter may beset to calculate new constants at time intervals other than fourteendays. It may calculate more or less often depending on the needs of thepatients. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the algorithm can utilize theadaptive filter to alter the coefficients for each patient for any oneof 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and 31 days.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the methods predict Ventricular Arrhythmia ina patient whereupon a physician can administer one or more commonlyprescribed antiarrhythmic drugs. A non-limiting, partial list ofantiarrhythmic drugs can include any one of Amiodarone (Cordarone,Pacerone), Bepridil Hydrochloride (Vascor), Disopyramide (Norpace),Dofetilide (Tikosyn), Dronedarone (Multaq), Flecainide (Tambocor),Ibutilide (Corvert), Lidocaine (Xylocaine), Procainamide (Procan,Procanbid), Propafenone (Rythmol), Propranolol (Inderal), Quinidine,Sotalol (Betapace), or Tocainide (Tonocarid). The drugs can be givenintravenously in an emergency situation or orally for long-termtreatment. Depending on clinical practice, in patients with atrialfibrillation, a blood thinner (anticoagulant or antiplatelet agent suchas aspirin) can also be added to reduce the risk of blood clots andstroke. However, the particular therapies are not critical to the first,second, third, fourth, fifth and sixth aspects of the invention and willdepend on a physician's or clinical judgment; whereas the likelihood ofa Ventricular Arrhythmia can be determined according to the methods ofthe first, second, third, fourth, fifth and sixth aspects of theinvention.

Processor and Communication System

The computing unit can be a specially adapted unit in order to carry outthe purposes and steps described herein. In any embodiment of the first,second, or third aspect of the invention, the sensors described hereincan operate in combination or conjunction with circuitry speciallyadapted to the purposes or steps described herein, or in combination orconjunction with more than one such processor, or in combination orconjunction with one or more elements of each type, such as for distinctsteps or portions thereof. The computing unit and the sensors whichdetect the data in each of the categories are specifically adaptedcomputers and processors configured or a medical or healthcare setting.The computers or processors can have shielded circuitry to preventelectric shock to a patient or operator. In any embodiment of the first,second, and third aspects of the invention, the computers and processorsof the present invention are not general purpose computers and can haveregulatory approval for approved medical use on patients.

One skilled in the art will recognize that the processor or anelectrical circuit used for calculating the methods of the first,second, third, fourth, fifth and sixth aspects of the invention can beintegrated into an implantable medical device or can be external to apatient. The methods of constructing such a processor based on themethods described herein are well known and can be fabricated by thoseof ordinary skill depending on the specific application. It is notedthat the present method can be advantageously used in the application ofbiomedical devices such as ICD and pacemakers, or any other biomedicaldevice having an electrode that can be used as a sensor. Where theprocessor is external to a device in which the battery is used, thedetection of physiological parameters may be made either wirelessly orwired, to the processor. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, the processor may be acomponent of a separate device, and may determine the algorithms of theinvention upon receiving the physiological parameters. For example, animplantable pacemaker, cardioverter, and/or defibrillator that providetherapy to the heart of a patient via electrodes can be advantageouslymonitored by the methods and apparatuses of the first, second, third,fourth, fifth and sixth aspects of the invention. In any configuration,the algorithm can use electrical excitation charges used in biomedicalelectrical stimulation devices to obtain physiological parameters or canpassively receive data on such parameters.

The present methods and systems can assist a programmer or clinician toschedule treatment such as administration of a pharmaceutical, dialysisor hemodialysis or determine the dosing or length of time of suchprocedures. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the physiological parameters whetherobtained by a sensor, manually entered, or from an electronic record ordatabase, can be entered into a processor or computer unit. From theobtained parameters, the value of the hazard function can be determinedby the processor or computing unit. The processor or computing unit canthen estimate the probability of a ventricular arrhythmia. The processoror computing unit can then use an obtained result at time t for thepatient, and further calculate a total error as the difference betweenthe total hazard and the actual results at time t. In any embodiment ofthe first, second, third, fourth, fifth and sixth aspects of theinvention, the algorithm can further adjust each of the coefficients tominimize the error. The algorithm can also utilize an adaptive filter ofany type known to those of skill in the art to alter the coefficientsfor each patient periodically. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the processoror computing can transmit the results to an output display. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the systems and methods of the system can transmit theresults to a programmer, server, clinician decision tool, 3rd partysoftware, physician or clinician. The obtained results can betransmitted via email, wirelessly, wired transmission, facsimile, or anysuitable means for transmitting such information whether via a graphicaluser interface, iPad or smartphone, or tablet application.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the processor contemplated may include any oneor more of a microprocessor, a controller, a digital signal processor(DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete orintegrated logic circuitry adapted for using the method of the first,second, third, fourth, fifth and sixth aspects of the invention. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the processor can include multiple components, such asany combination of one or more microprocessors, one or more controllers,one or more DSPs, one or more ASICs, or one or more FPGAs, as well asother discrete or integrated logic circuitry. The functions attributedto the processor may be embodied as software, firmware, hardware or anycombination thereof. In particular, any processor contemplated by thefirst, second, third, fourth, fifth and sixth aspects of the inventioncan have a microprocessor configured to obtain the physiologicalparameters and make the necessary calculations for the hazard functionstored in memory. In particular, the processor may receive data fromsensors contained in an implantable biomedical device while operating ina patient and described by the present formulae of the invention andprovide alerts or messaging via wireless control to base moduleconnected wirelessly to the implantable device. In any embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the invention,the device may provide an audible alert or signal an alert during aroutine monitoring session. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, a user and/orclinician can interface and receive the results of the algorithmincluding the probability of Ventricular Arrhythmia and the calculatedhazard function. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the adaptive filter can beused to calculate the optimal number and type of physiologicalparameters.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the communication system allows transferringany data, including the data from the sensors to the processing unit.The communication system also allows the communication of the estimatedarrhythmia risks and patient outcomes. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, atelemetry module having any suitable hardware, firmware, software or anycombination thereof known to those of ordinary skill for communicatingwith another device, can transmit the probability of VentricularArrhythmia and the calculated hazard function or any other calculationobtained from the medical device or data records. Under the control ofthe processor, the telemetry module can receive downlink telemetry fromand send uplink telemetry to a programmer or a base module with the aidof an antenna, which may be internal and/or external. The processor canalso provide the data to be uplinked and the control signals for thetelemetry circuit within the telemetry module via an address/data bus.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the telemetry module can provide received datato the processor via a multiplexer or any other suitable methods andsystems. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, a telemetry module can communicatewith the processor in the implanted biomedical device using RFcommunication techniques supported by telemetry modules known to thoseof ordinary skill.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the processor can transmit the probability ofVentricular Arrhythmia and the calculated hazard function and any otherobtained measurement or calculated values to a programmer or basemodule. In any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, the programmer or base module mayelectrically interrogate the processor or implantable biomedical deviceas needed. The processor and/or implanted biomedical device can storethe probability of Ventricular Arrhythmia and the calculated hazardfunction and/or calculated values within memory and retrieve the storedvalues from memory upon receiving an instruction from a programmer orbase module. The processor may also generate and store data containingthe obtained calculations based on the measurements collected from thesensors or data records and transmit the data to programmer or basemodule upon receiving instructions. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, datafrom the sensors, the processor, or implanted biomedical device may beuploaded to a remote server on a regular or non-regular basis where aclinician or program may access the data to determine whether apotential life threatening or hazardous event due to the probability ofVentricular Arrhythmia and the calculated hazard function exists. Anexample of a remote server includes the CareLink Network, available fromMedtronic, Inc. of Minneapolis, Minn.

The technical benefits of the real-time monitoring described hereinprovide for immediate adjustments to the hazard functions that cannot beaccomplished with pen and paper. The changes to the parameters andtherefore the changes to the hazard level occur constantly in order tocontinuously update the hazard level of the patient. These changes occurtoo quickly for the calculations to be performed with the use of pen andpaper.

The processors described herein can be medical device processors.Medical device processors can control the collection of external orimplantable medical device data, control the collection of metadatabased on the collected data, and synchronize the data on a timeline. Thecomputing unit and the sensors which detect the data in each of thecategories are specific purpose computers and processors configured or amedical or healthcare setting. The computers or processors can haveshielded circuitry to prevent electric shock to a patient or operator.In any embodiment of the first, second or third aspects of theinvention, the computers and processors of the present invention are notgeneral purpose computers and can have regulatory approval for approvedmedical use on patients. The processors also have communication systems,hardware and software that protect patient privacy by protecting theinformation obtained from the patient.

The systems described herein can also obtain historical data fromelectronic medical records or other sources. The hardware configurationsof the system allow for transmission of the data obtained to thepatient's electronic medical records, or to a hospital data hub,handheld device, or monitor. The computers or processors describedherein are specially adapted to receive patient data from the sensorsand immediately perform the necessary calculations to determine theprobability of a life threatening ventricular arrhythmia.

Sensors

The first, second, third, fourth, fifth and sixth aspects of theinvention can have a set of sensors on implantable or external devicesto gather physiological parameters from the patient. The sensors can bein communicated or be a part of communication system, which relaysinformation and data between the device, the processing unit, andoptionally the patient and the medical care personnel, and a computingunit to process the information. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the device canbe either implantable or non-implantable depending on the therapy beingdelivered or the availability of a particular device to obtain data foruse in the algorithms of the first, second, third, fourth, fifth andsixth aspects of the invention. For example, an external blood pressurecuff obtaining a digital reading during a therapy session can be used tocollect blood pressure data for use by the processor whereas animplanted pacemaker can obtain ECG data in vivo. In any embodiment ofthe first, second, third, fourth, fifth and sixth aspects of theinvention, the device may be specially designed to obtain specific typesof data such as an EMG and in other cases can be obtained fromnon-specifically designed devices such as from the electrodes of an ICD.In general, it will be understood that the sensors used in the first,second, third, fourth, fifth and sixth aspects of the invention can beintegral, separate, specially designed or not specially designed toobtain the physiological parameters contemplated by the first, second,third, fourth, fifth and sixth aspects of the invention. The onlyrequirement is that the sensors in whatever form, provide the sources ofdata necessary for the processing unit to interpret. In one specificexample, an implantable medical device, such as cardiac pacemakers,implantable defibrillators and implantable loop recorders can be used tocollect data from patients and search for correlations between thephysiological signals and patient outcomes, such as a VentricularArrhythmia. Electrical impedance of the tissue, number of spontaneousVentricular Tachycardia (VT) and Ventricular Fibrillation (VF) eventsper day, time spent in Atrial Fibrillation (AF), and information aboutthe patient's dialysis situation can all be used as factors contributingto the patient's outcome.

The number of spontaneous Ventricular Tachycardia (VT) and VentricularFibrillation (VF), collectively defined as VT/VF events, experienced bythe patient in a given day can be correlated to the parasympatheticstimulus that the patient has been receiving: the parasympatheticnervous system (PNS) controlling certain aspects of bodily excretion. Anincrease in the number of spontaneous VT/VF events experienced by thepatient in a given day can also indicate an increased trend toward aLTVA. Moreover, Atrial Fibrillation (AF) can also lead to VentricularArrhythmias that can be life threatening, e.g. LTVA. An increase in theamount of time spent in AF in a given day, also known as AF burden, canalso indicate an increased trend toward a LTVA. Moreover, high levels ofpotassium in the blood can lead to an increased occurrence ofVentricular Arrhythmias.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the electrical impedance of a tissue can bemeasured by an implantable device. The impedance can be measured by anyof one, two, three, four or more electrodes. In an exemplary oneelectrode system, the single electrode can be used for both excitationand measurement such as a pacemaker with a bipolar pacing lead. In thecase of a pacemaker having a bipolar pacing lead, excitation can beapplied between a ring electrode. The measurement can be then takenbetween the electrode tip and the case wherein the pacemaker case servesas the common electrode. An example of a pacemaker with a bipolar pacinglead is described in U.S. Pat. No. 5,843,135, the contents of which areincorporated herein in their entirety. As shown in FIG. 10, a pacemakerdevice 1100 can be connected at an output 1107 to a lead 1101. The lead1101 can extend into the heart and has a tip electrode 1104 and a sensor1105. The lead 1101 only has a single conductor 1102 to send pacingpulses to tip electrode 1104. The lead 1101 can have a casing 1103 whichruns the length of the lead 1101 from the proximal end where it connectsto the output 1107 to the distal end where it connects to the tipelectrode 1104. The pacemaker provides output pulses from a pulsegenerator 1106 to the lead 1101. The pulse generator 1106 can becontrolled by a timing logic and control block 1109. This block 1109 canbe in two way communication with a transmit and receive circuit 1111,which is in telemetric or electrical communication with an externalprogrammer (not shown). Sense processing circuitry 1108 can receivesignals from the lead 1101. This circuitry 1108 receives signals andcontains other circuitry for the separation of cardiac and sensorinformation. A square wave current generator 1110 can also be inelectrical communication with the timing logic and control block 1109.

In an exemplary two electrode system, such as an implantable looprecorder, e.g. Medtronic Reveal, two electrodes can be used both forexcitation and for measurement of electrical signals. Aspects of theReveal insertable loop recorder are disclosed in commonly assigned U.S.Pat. Nos. 5,987,352 and 6,230,059, the contents of which areincorporated herein in their entirety. As shown in FIG. 11, a medicaldevice can have a monitor 1201 implanted in the upper thoracic region ofthe patient's body, displaced from the patient's heart 1206. The devicecan have a non-conductive header module 1202 attached to a hermeticallysealed enclosure 1205. The enclosure 1205 can contain the operatingsystem of the device. A first subcutaneous electrode 1203 can be formedon the header module 1202, and a second subcutaneous electrode 1204 canbe formed by an exposed portion of the enclosure. The conductive housingelectrode 1204 can be directly connected to sensing circuitry, while afeed through extends through the mating surfaces of the header module1202 and the enclosure 1205 to connect the first electrode 1203 tosensing circuitry.

In exemplary three electrode systems, two electrodes can be used forexcitation and one can be used for measurement. A three electrode systemcapable of measuring impedance is shown in FIG. 13. Lead 1401 can haveelectrodes 1402, 1403, and 1404, and can attach to an impedancemeasurement module 1407. The electrodes 1402, 1403, and 1404, andimpedance measurement module 1407 can be in electronic communicationwith and can be controlled by a switching system 1405. The switchingsystem 1405 can be in electronic communication with a stimulationcircuit 1406 and a processor 1408. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, theprocessor 1408 can be connected directly to the switching system 1405.For measuring impedance, two of the electrodes 1403 and 1404 can be usedfor stimulation connected to the impedance measurement module 1407. Thelead 1401 can be connected to both the impedance measurement module 1407as well as the switching system 1405. The third electrode 1402 can beused for measurement and can be connected to the lead 1401 wherein theimpedance measurement module 1407 collects data from the third electrode1402.

In an exemplary four electrode system, a separate pair of electrodes canbe used for both excitation and measurement; such systems can be morecommonly used in external devices. One example of a four electrodesystem is shown in FIG. 14. Lead 1501 contains four electrodes, 1502,1503, 1504, and 1505. The electrodes 1502, 1503, 1504, and 1505 can beelectronically connected to switching arrays 1506 and 1507 locatedwithin the impedance measurement module 1509. A source generator 1508can be in direct electrical communication with the switching array 1507.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the source generator 1508 can be in directelectrical communication with the switching array 1506, the particulararrangement depending upon which pair of electrodes is being used toprovide electrical stimulation. The impedance can be measured betweenany two of the electrodes as determined by the switching arrays 1506 and1507. The information from the impedance measurements from impedancemeasurement module 1509 can then be transferred to the processor 1510.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the processor 1510 can be connected directlyto the switching system 1506.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, electrical excitation can be in the form of asinusoidal or in the form of a step function, e.g. pulse. Thefrequencies for such signals can range from 16 Hz to 200 KHz, with acurrent amplitude of 10 micro-Amps or more. In any embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the invention,excitation can be generated by a constant current or a constant voltagesource, and can be applied continuously or periodically to prolong thebattery life. Circuitry within the implantable device can measure theresponse from the tissue and calculate the magnitude of the electricalimpedance, which has a base value usually in the range of 100 Ohms to1,000 Ohms. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the electrical impedance can rangefrom any of 25 to 100, 25 to 250, 25 to 500, 25 to 1,000, 25 to 1,250,50 to 100, 50 to 250, 50 to 500, 50 to 1,000, 50 to 1,250, 100 to 250,100 to 500, 100 to 1,250, 100 to 1,500, 500 to 1,000, 500 to 1,500, 500to 2,000, 750 to 1,250, 750 to 1,500, 750 to 2,000, 1,000 to 1,250,1,000 to 1,500, 1,000 to 2,000, 1,000 to 2,500, and 1,500 to 2,000 Ohms.The expected changes in the tissue impedance are usually less than 10%of its base value. Any such expected changes of a calculated value arecontemplated by the first, second, third, fourth, fifth and sixthaspects of the invention. An implantable device can store an entireimpedance trace or a representative value, such as a daily average, forfurther processing by the computing unit. Alternatively, the implantabledevice may transmit the raw data to an external unit, and the externalunit may store and process that data.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, a spontaneous VT/VF event can be detected byan ECG processing circuitry of the device. The ECG can be sensed by theelectrodes on the device as performed by an implantable loop recordersuch as Medtronic Reveal and leadless pacing systems that can bedeployed in the right ventricle, or with the aid of electrodes on theleads as in the case of implantable pacemakers, defibrillators, cardiacresynchronization devices or subcutaneous defibrillators. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, ventricular rhythm can be classified by the device orprocessor based on certain programming parameters. For example, aphysician or programmer may program the device to classify three or moresequential beats that originate from the ventricle at a rate of morethan 100 beats per minute as a Ventricular Tachycardia (VT). Animplantable device may record the entire electrogram trace for theVentricular Arrhythmic (VA) event, or some information such as the rateand duration of the arrhythmia, or simply the number of arrhythmicevents in a given day.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, Atrial Tachycardia (AT) and AtrialFibrillation (AF) can be detected by the ECG processing circuitry of theimplantable device. As described above, the ECG can be sensed solely bythe electrodes placed on the device or with the aid of electrodes thatare on the leads attached to the implantable medical device. In anyconfiguration, a lead can be attached to the atrium where theimplantable medical device senses the electrical activity of the atriumand processes the signals to detect atrial arrhythmias such as AT andAF. When there is no atrial lead, the detection of AT can use a pseudoECG recorded by the electrodes on the implantable medical device bysearching for P-waves or monitoring a ventricular electrogram forirregularities caused by atrial arrhythmias conducted from an atrium toa ventricle. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the implantable device canrecord the entire electrogram trace for the atrial arrhythmic event, orobtain information such as the rate and duration of the arrhythmia, orthe total time spent in AT/AF during a given day.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, information related to whether hemodialysistreatment was received during a day can be entered manually,communicated electronically from a medical system, such as an electronichealth record or the dialysis management system, or imputed by theimplantable medical device. Although manual entry and the retrieval froman electronic system are simple options, they may not be alwaysavailable. In that situation, the implantable medical device can inspectthe daily impedance values and interpret a rise in the impedance valueas an indication of a reduction of the fluid volumes resulting from adialysis session. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the processing unit can checkthe calendar to determine if the patient is scheduled to receivedialysis on a particular day. Insofar as most dialysis patients maintaina fixed schedule, such as receiving three dialysis sessions per week oneither Monday, Wednesday, and Friday or Tuesday, Thursday, and Saturdayof each week, the processing unit can store data according to suchschedules or any combination of days thereof.

Example 1

A database containing transmissions from patients with implantablemedical devices was used. In this database, a search was carried out tosearch for individuals having a weekly pattern in their electricalimpedance trace as shown in FIG. 4 a, which shows the daily impedance201 plotted against time in days 202. Without being limited to anytheory, it is hypothesized that mild fluid overloads are experiencedduring the long interdialytic period of the hemodialysis patients,usually on the weekends, manifest themselves as drops in the impedancevalues on Sundays. The presence of a weekly pattern in the dailyimpedance was confirmed by a peak at a frequency of 1/7 reciprocal daysin the power spectrum, as shown in FIG. 4 b, which plots the power 203against reciprocal days 204. The arrow at 205 corresponds to a peakfrequency at 1/7 days. At the end, 1,257 patients with a weeklyimpedance pattern were identified in the dataset. FIG. 5 shows a plot ofthe number of patients 308 against the day of the week the patient hadthe lowest impedance value 309. 301 refers to Sunday, 302 to Monday, 303to Tuesday, 304 to Wednesday, 305 to Thursday, 306 to Friday, and 307 toSaturday. The graph shows that majority of these patients, 616, havetheir lowest impedance value corresponding to a volume overload at theend of the weekend, i.e. on Sundays. This fact allows us to infer thatfor these patients, Sunday corresponds to the longest time period sincetheir last dialysis session.

A closer review of the data from the 616 patients having their lowestdaily impedance values on Sundays showed that some patient had missingdata which would negatively impact the analysis. Once those patientswere removed from the population, a group comprising 593 patients wasobtained for the final analysis. Amoeba algorithm applied to the dataextracted from all 593 patients selected for the analysis to build anon-adaptive predictive model. One set of values for the ninecoefficients, k1 through k9, was determined by minimizing the predictionerror for each patient. Afterwards, mean and median values weredetermined for each of the nine coefficients, k1 through k9. Finally,the model was run with these fixed coefficients and the outcomes wereinspected. Results of this evaluation are shown in FIGS. 6 a, 6 b and 6c.

FIG. 6 a shows the results where the mean values of the ninecoefficients were used. The box labeled 401 shows the number of truepositive results, 402 shows the number of false positive results, 403shows the number of the false negative results, and 404 shows the numberof the true negative results. FIG. 6 b shows the results when the medianvalues of the nine coefficients are used. The box labeled 501 shows thenumber of true positive results, 502 shows the number of false positiveresults, 503 shows the number of the false negative results, and 504shows the number of the true negative results. FIG. 6 c shows thepresent situation where hemodialysis patients do not receive devicebased treatments.

To help interpret the performance of the non-adaptive algorithm, thecurrent medical treatment option was also evaluated, and is shown inFIG. 6 c. The box labeled 601 shows the number of true positive results,602 shows the number of false positive results, 603 shows the number offalse negative results and 604 shows the number of true negativeresults. Because under the current situation patients do not receivedevice based treatments, all the patients are placed in the negativeboxes, 603 and 604. This gives a sensitivity, defined as Sensor equalsthe number of true positives divided by the sum of true positives andthe number of false negatives of 0%. This can give a specificity definedas Specificity equals the number of true negatives divided by the sum ofthe number of true negatives and the number of false positives of 100%.The positive predictive value of the current treatment option, definedas PPV equals the number of true positives divided by the sum of thenumber of true positives and the number of false positives is undefined,as no patient is selected for treatment. The negative predictive value,defined as the number of true negatives divided by the sum of the numberof true negatives and the number of false negatives can be 81.2%.

By contrast, and in one specific example, the sensitivity when using themean values of the coefficients is 37.96%, and the specificity is72.96%. This method gives a positive predictive value of 23.16% and anegative predictive value of 16.11%. When the median values of thecoefficients are used, the sensitivity is 71.30%, with a specificity of30.52%. This gives a positive predictive value of 18.60% and a negativepredictive value of 17.32%.

In this trivial case, sensitivity of the test is zero, as no patient isselected for treatment. In addition, specificity was shown to be 100%,as all the patients who do not need treatment are correctly deniedtherapy. These observations bring out few important points:

(1) by doing no test, one can achieve 100% specificity because nopatient is selected for treatment. Therefore, any new test would have adifficult time measuring up to the status quo if the goal improvesspecificity; and (2) Since the no test results in the selection of nopatients, sensitivity value is zero, meaning that none of the patientsneeding treatment can be being identified. Hence, improvements inpatient identification can be achieved easily by almost any test.

Once the model using the adaptive filter was applied to the data fromthe group of 593 patients, the results shown in FIGS. 7 a and 7 b wereobtained. Briefly, when the initial values given to the model were themean values of the parameters determined earlier, the results shown inFIG. 7 a were obtained. The box labeled 701 shows the number of truepositive results, 702 shows the number of false positive results, 703shows the number of false negative results and 704 shows the number oftrue negative results. Similarly, when the median values of theparameters determined earlier were used as the starting values, theresults shown in FIG. 7 b were found. The box labeled 801 shows thenumber of true positive results, 802 shows the number of false positiveresults, 803 shows the number of false negative results and 804 showsthe number of true negative results.

Using the mean values of the coefficients as the initial values give asensitivity of 39.81% and a specificity of 73.40%. This method had apositive predictive value of 25.00% and a negative predictive value of15.14%. Using the median values of the coefficients as the initialvalues gave a sensitivity 67.59% and a specificity of 67.27%. Thismethod had a positive predictive value of 28.52% and a negativepredictive value of 10.39%.

Results shown in FIG. 7 can be interpreted by focusing on the data shownin FIG. 7 b. In that case, the model was started with a fixed set ofvalues for the parameters k1, k2, k3, k4, k5, k6, k7, k8, and k9, any ofwhich could vary at each step along the way to reduce the error at thetime. This method produced a test with sensitivity and specificityvalues over 60%. As mentioned earlier, the focus can be on thesensitivity measure, which was calculated to be 67.58% in this example.However, the number needed to treat (NNT) to save a life is(73+183)/73≈3.5, which is a reasonable number for a lifesaving therapy.The NNT value is significantly lower than what it would have been if allthe patients were to be treated, that is (108+485)/108≈5.5.

The study was carried out using electrical impedance of the tissue,number of spontaneous ventricular tachycardia and ventricularfibrillation events per day, time spent in atrial fibrillation andinformation about patient's dialysis situation as the input parameters.The information about the patient's dialysis situation was inferred bythe periodic fall of the tissue impedance measurements.

The information obtained by the sensors and algorithm can be integratedinto a system for remote monitoring and analysis, allowing easier usefor the patient and caregivers. One example of such a system is theMedtronic CareLink® Network and DWAS system. This system is described inFIG. 8. The information from the implantable device 905 can betransmitted to a remote monitor 906, which can then upload theinformation into the system. This data can then be stored withinformation from all data sources 901, including enrollment data 907,follow-up data 908, device and registrant tracking data 909, consumerdata 910, and other data sources 911. The data can then be transferredto a data collection subsystem 902. This subsystem can extract,transform and load the data 912, into a source warehouse 913. Copies ofthe data subsets 914 can be distributed by the data distributionsubsystem 903, taking into account appropriate business rules 915 andprivacy laws 916. This information can then be transferred to data marts917. The data from the data marts 917 can then be transferred to a dataanalysis subsystem 904. The data analysis subsystem then makes datainferences 918, for each of the data marts to give the analyzable data919, 920 and 921. The data can then be analyzed, including any analyticsapplications 925, to give analyzed information 922, 923, and 924. All ofthis can be done utilizing the Data Warehousing and Analytics Services(DWAS) components 926.

One benefit of utilizing a system such as the Medtronic CareLink®Network is the ease of access to information. FIG. 9 shows a screen shotof a search of patients using the system. The user need only enter theinformation required, such as patient age 1001, type of device 1002, ordevice model 1003, and the length of time for which to search 1004, anda DWAS system can select the patients that match the criteria andprovide the necessary data. This allows for analysis of trends, and datanecessary for calculating the coefficients based on an aggregate groupof similar patients.

One skilled in the art will realize that it is possible to use less thanfour parameters to estimate the risk of a life threatening ventriculararrhythmia. The first, second, third, fourth, fifth and sixth aspects ofthe invention can work with fewer parameters, albeit with some loss insensitivity and selectivity. Additionally, other factors that correlateto a higher risk of a life threatening ventricular arrhythmia may beadded to the first, second, third, fourth, fifth and sixth aspects ofthe invention without going beyond its scope.

Real time information from a patient 1301 may be obtained during adialysis session as shown in FIG. 12. The patient 1301 may have animplantable medical device 1302, examples of which are described herein.The dialysis system 1305 can be connected to the patient via blood lines1303 and 1304. The dialysis system 1305 may include sensors detectingany number of combination of physiological parameters contemplated bythe first, second, third, fourth, fifth and sixth aspects of theinvention including, but not limited to mixed venous oxygen saturation,the amount of fluid removed, and dialysis markers such as sodium orpotassium using any of the sensors described herein or known to one ofordinary skill for its intended purpose for collecting the relevantdata. The information detected by the dialysis system 1305 can bedirectly uploaded to a processor 1306 wirelessly or through a wiredconnection, or the information can be read by a user (not shown) andmanually entered into the processor 1306. In any embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the invention,information can be obtained from the implanted medical device 1302 andcollected by a receiver 1308 and either directly uploaded into theprocessor 1306 wirelessly or through a wired connection, or theinformation read by the user and manually entered into the computer.

It will be apparent to one skilled in the art that various combinationsand/or modifications and variations can be made in the dialysis systemdepending upon the specific needs for operation. Features illustrated ordescribed as being part of one embodiment may be used on anotherembodiment to yield a still further embodiment.

Medical Monitoring System

The first, second, third, fourth, fifth and sixth aspects of theinvention provide for medical monitoring system having one or moreimplantable medical devices capable of detecting and monitoring avariety of patient medical parameters, as defined herein. Several ofthese parameters, such as fluid level and incidence of arrhythmia can beaffected by dialysis. In order to provide more efficient and precisedialysis treatment and avoid the incidence and/or risk of arrhythmia,the effects of a dialysis session on a patient's physiologicalparameters can be monitored, analyzed, and displayed. The first, second,third, fourth, fifth and sixth aspects of the invention can accomplishthis by providing at least one output from an implantable ornon-implantable medical device collecting patient data, which can thenbe synchronized and/or simultaneously presented with at least onemedical parameter and the occurrence of a dialysis session. The dialysissystems or method of the first, second or third inventions can alsodisplay dialysis parameter data related to the occurrence of arrhythmia,in conjunction with any medically and clinically pertinent informationsuch as a patient's name, age, electronic medical number. In particular,the relationship between dialysis and physiological data can beespecially important and require computation using specific dialysisalgorithms.

The synchronized and/or simultaneous presentation of data allows for theanalysis and detection of arrhythmia due to the effects of dialysis. Thefirst, second, third, fourth, fifth and sixth aspects of the inventioncan lead to improved clinical assessment, personalization orcustomization of therapy delivery, improved therapeutic delivery, andbetter clinical outcomes and provides significantly more than simplyaggregating information. The systems and methods of the first, second,third, fourth, fifth and sixth aspects of the invention further providefor computer-assisted methods for dialysis algorithms capable ofimplementing clinical action on networked health systems in a secure,HIPAA compliant environment based on any of the collected data. Thesystems and methods of the first, second, third, fourth, fifth and sixthaspects of the invention can further connect patient data to anElectronic Medical Record (EMR) providing historical data to assist infuture dialysis sessions, or can call and receive information from theEMR.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the inventions, data can be collected from a patient inbetween dialysis sessions. The data can be collected continuously or inpre-programmed time intervals. The first, second, third, fourth, fifthand sixth aspects of the invention provide for collecting medicalparameter data including the non-limiting group consisting ofelectromyogram (EMG), an electroencephalogram (EEG), anelectrocardiogram (ECG), tissue impedance, blood pressure, the level ofspecific ions in the blood of a patient, or other data concerning thehealth of the patient. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, the systems andmethods can analyze the collected data and provide or recommendadjustment of a patient's dialysis prescription. The adjustment can beprovided in the form of computer instructions to specially adapteddialysis systems configured to effectuate a change in any number ofdialysis parameters such as time of dialysis, flow rate, ion or soluteconcentration in the dialysis, time in between or number of dialysissessions, or any parameter impacting a patient's physiological responseto dialysis known to those of ordinary skill

The collected data can be presented in synchronized and/or simultaneousform wherein the collection, presentation, and analysis of such data canassist in evaluation and possible subsequent modification of dialysisinduced stressors to reduce incidence and risk of arrhythmia and/orsudden cardiac death (SCD). Aggregating such data, e.g., ECG, isnon-trivial and cannot be performed by pencil and paper. The dialysissystems and methods of the first, second, third, fourth, fifth and sixthaspects of the inventions are implemented on specifically adaptedcomputers and processors configured for a medical or healthcare setting.The computers and processors can have shielded circuitry to preventelectric shock to a patient or an operator. In any embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the inventions,the computers and processors are not general purpose computers and canhave regulatory approval for approved medical use on patients. Thecomputer and processors can be use secure communication channelsspecially adapted to a particular medical device.

In the implanted sensors of the first, second, third, fourth, fifth andsixth aspects of the invention, a critical issue can be battery life.The more sensing and data streaming is required, the faster a batterymay be depleted. As such, the first, second, third, fourth, fifth andsixth aspects of the invention can use specially adapted computers,processors and systems to preserve battery life, and to reduce orotherwise optimize an amount of time required for recharging. In anyembodiment, the first, second, third, fourth, fifth and sixth aspects ofthe invention contemplate a safety window for battery life wherein datasensed from a patient having an implantable sensor is not lost. Inparticular, a prioritization schedule of parameters can be implementedto sense and stream data in order to maintain a battery reserve. Forexample, as a battery charge reaches a certain minimum threshold, thesensing performed by the implanted sensor can be reduced or performedintermittently at a reduced rate to preserve charge.

FIG. 15 shows one, non-limiting embodiment of the first, second, third,fourth, fifth and sixth aspects of the inventions. The output from themedical monitoring system in FIG. 15 is a chart showing the time apatient spent in arrhythmia and the occurrence of a dialysis session.Each hash on the x-axis of the chart corresponds to a period of about 30minutes. The y-axis shows the time spent in arrhythmia in each timeperiod. As can be seen in FIG. 15, the incidence of arrhythmia wasincreased for the patient in the time period following dialysis. Suchdata is important to the health care providers, as the data shows howthe patient responds to the dialysis session. If the patient experiencesabnormal amounts of arrhythmia events in the time period after adialysis session, a need to change dialysis parameters and or drugregimens in order to improve patient outcome may be indicated. If thepatient experiences abnormal amounts of arrhythmia events in the timeperiod immediately before dialysis, the arrhythmia events could indicatethat the patient was experiencing fluid overload between dialysissessions, possibly indicating that not enough fluid was removed from thepatient during the previous dialysis session. The length of time beforeand after dialysis could be increased or decreased based on theparticular time period of interest of the health care provider, and neednot be six hours. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the data for about 1-4 hoursbefore a dialysis session can be displayed if this data is of more valueto the user. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, up to 8 hours of data afterdialysis can be displayed. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the user canselect the time period before or after each dialysis session to bedisplayed. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the monitoring of the specificparameters can be constant, but the display can show only the timeperiods the user is interested in. In any embodiments of the first,second, third, fourth, fifth and sixth aspects of the invention, furtherarrhythmia information can be collected and displayed on the same chart.For example, the height of the bars shown in FIG. 15 can represent thetotal number of times an arrhythmia started and stopped during the timeperiod, or the number of minutes out of the time period that the patientspent in arrhythmia. The arrhythmia rate can also be included in thedata. Further information can include the number of arrhythmia events,the types of arrhythmia or the arrhythmia rate. This information can beincluded on the same chart by using colors or symbols to indicate thedata.

The output shown in FIG. 15 includes a four hour dialysis session. Fourhours is a common length of time for a dialysis session. However, oneskilled in the art will understand that a dialysis session of any lengthof time can be shown in the output of the first, second, or thirdinvention. For at-home dialysis, sessions may often be longer, includingin the range of 6-8 hours. No matter the length of the dialysis session,the entire session can be shown using the display of the first, second,third, fourth, fifth and sixth aspects of the invention.

By using the data shown in FIG. 15, the user can determine if theprevious dialysis session resulted in arrhythmias. If the previousdialysis session resulted in arrhythmias, the system of the first,second, third, fourth, fifth and sixth aspects of the invention can warnthe health care provider if the patient started over the target fluidlevel, had excess fluid removed, if the session ended below the targetfluid level or any other parameters that may be out of range. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, these parameters can be included in the same output asarrhythmia data. For example, out of range parameters can be noted by asymbol or color change in the chart. The output can also note whetherarrhythmias have occurred just prior to the current dialysis session. Ifhigh fluid levels are noted as well, the high fluid levels could informor warn the health care provider that extra fluid may have causedarrhythmias in the patient, and that the patient should fluid removed toa new target level.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the frequency of measurements of arrhythmia orany other medical parameter can be adjusted based on the observedfrequency of arrhythmia events. That is, if the frequency of arrhythmiais found to be increased during a specific time period, the frequency ofmeasurements taken by one or more other medical parameters can beincreased during the same time period. This can provide additional dataon the underlying cause of the arrhythmia events.

FIG. 16 shows an output including the hours spent in atrial fibrillationper day. The dark lines on the chart in FIG. 16 show the occurrence of adialysis session. Data from multiple dialysis sessions can be analyzedand the relative risk of arrhythmic event can be displayed for eachperiod of time. As can be seen in FIG. 16, the incidence of atrialfibrillation is increased in the time soon after each dialysis session.The chart in FIG. 16 also shows that the patient had a longinterdialytic window during the week, going three days without receivingdialysis, from day 4 to day 7. This can be a common occurrence indialysis patients, as routine dialysis is often not performed onweekends. After going three days without dialysis, the incidence ofatrial fibrillation increased significantly. By providing the medicalparameters in this fashion, a care giver can observe the increasedincidence of atrial fibrillation due to a longer period withoutdialysis. The information can provide clinically relevant informationconcerning the optimal frequency of dialysis for the particular patient,by allowing physicians to see the temporal link between the dialysis andobserved arrhythmias. A physician can take steps to reduce theoccurrence of arrhythmia. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, the specially adaptedcomputer and processors used for dialysis can be programmed withalgorithms that can adjust one or more dialysis parameters in responseto the observed synchronized data. Observation of arrhythmia occurrencesbefore a dialysis session coupled with higher than normal pre-dialysisfluid levels could trigger a longer dialysis session to remove excessfluid. Arrhythmias before a dialysis session without higher than normalpre-dialysis fluid levels could trigger use of lower potassium (or otherion) dialysate concentrations in response to a probable high potassium(or other ion) blood concentration. An observed increase in heart rateor arrhythmia occurrence after the previous session may signal a need tonot remove as much fluid as the previous session or to not remove fluidto the same level as in the previous session.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the frequency of measurements of medicalparameters such as blood ion concentration or fluid level can beadjusted based on the time duration of arrhythmia events. If theobserved arrhythmia events are of a longer time, then the frequency ofmeasurements of other medical parameters can be increased to provideadditional information as to the cause of the arrhythmia.

If the incidence of atrial fibrillation is increased during the timeperiods immediately before or after a dialysis session, the increase inatrial fibrillation would indicate that the events are linked to thedialysis sessions. This information can prompt the health care providerto adjust dialysis session parameters, such as the rate ofultrafiltration or the concentration of solutes provided to the patientduring the session. If the incidence of atrial fibrillation is increasedduring the longer interdialytic window, as shown in FIG. 16, the atrialfibrillation may be caused by fluid overload, and the health careprovider may add extra dialysis sessions each week in order to avoidlong interdialytic windows.

Changes to dialysis parameters can be selected by choosing a lessaggressive method to achieving dry weight, such as a lower blood flow orlower ultrafiltration rate, and then determining the effect of changingthe dialysis parameter on arrhythmic events or incidence of atrialfibrillation. If dry weight is determined by fluid measurement, such asby sensing the impedance of a patient, a closed loop optimization can becreated to create a smoother fluid removal profile and/or stop therapyif there is a spike in impedance or rise in heart rate driven by volumedepletion. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, changes to dialysis parameters canbe determined by a computer. One skilled in the art will understand thatalgorithms can be created to highlight trends in the medical parametersdue to dialysis parameters. Based on the timing of arrhythmic eventswith respect to dialysis, the computer can automatically cause thedialysis system to change the ultrafiltration rate, blood flow rate orfluid removal target. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, a computer can makesuggestions to the health care providers based on the data.

FIG. 17 shows a sample output including the incidence of arrhythmia. Thestars on the chart are the times when an arrhythmia was detected. Thedark bars show the occurrence of a dialysis session. As can be seen inFIG. 17, after a longer interdialytic window, that is, after three dayswithout dialysis, between days 4 and 7, the incidence of arrhythmia wasincreased. This display allows the doctors to determine the effect of alonger period of time between dialysis sessions. Arrhythmias may belinked to factors resulting from dialysis, or arrhythmias can be linkedto factors that are not related to dialysis. Providing the arrhythmiadata with the occurrence of dialysis sessions allows the health careproviders to determine the link between the patient's arrhythmias andthe dialysis. An increase in arrhythmia prior to a dialysis session,especially after a long interdialytic window, may mean that thearrhythmias are being caused by fluid overload or excessive ion ormetabolite build-up during these periods. As a result, the frequency ofdialysis could be increased to reduce the occurrence of arrhythmia.Alternatively, more fluid can be removed from the patient duringdialysis. The patient's estimated dry weight can be altered in order toremove additional fluid and decrease fluid overload between dialysissessions. This is especially true if the patient is not experiencingarrhythmias in the periods after dialysis. If the patient isexperiencing arrhythmia during or after dialysis, the dialysisparameters can be adjusted to reduce the amount of fluid removed, orreduce the rate at which fluid is removed.

FIG. 18 shows a sample output according to another embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the invention.The output from the medical monitoring system in FIG. 18 shows therelative fluid level of a patient for a time period including the sixhours before a dialysis session, during the dialysis session, and in thesix hours after a dialysis session, shown as the inverse of impedance.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the relative fluid level of the patient canalso be determined by the pre-dialysis and post-dialysis weight of thepatient. As is shown in FIG. 18, and as expected, the occurrence of adialysis session caused a marked change in the relative fluid level ofthe patient. Fluid in the patient accumulated slowly before the dialysissession, and then quickly dropped during the dialysis session, beforeslowly increasing after dialysis. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention,arrhythmia or other data such as patient reported symptoms can besuperimposed over the fluid level data shown in FIG. 18. Includingarrhythmia data on the same chart as fluid level can be useful forclinical diagnosis because the additional information can help to showthe link between fluid level and arrhythmia for the particular patient.

In any embodiment of the s first, second, third, fourth, fifth and sixthaspects of the invention, the frequency of measurements by the medicalmonitoring system can be adjusted based on the fluid level of thepatient. For example, if the patient fluid level is significantly higheror lower than the expected patient fluid level, the frequency ofmeasurements for other medical parameters can be increased. Because thepatient fluid level can effect arrhythmia or other medical parameters,the frequency of measurements of these medical parameters can be changedwhen the fluid level of the patient changes.

FIG. 19 shows the effect of fluid level on the measured impedance of asubject. The charts in FIG. 19 show the results of a study measuring thenormalized impedance on two different animals with respect to fluidlevel over the course of several weeks. In each of the charts shown, 1 Lof fluid was given to each of the animals each week. As can be seen,after the addition of fluid, the impedance levels dropped dramatically.As urine was excreted by the animals, the impedance levels began to riseback to the normalized level, shown as 1.0 on each of the charts. FIG.19 shows that the relative fluid level of a patient can be determinedbased on the measured impedance. Drops in impedance correspond toincreases in the relative fluid level of the patient.

FIGS. 20 a-20 e show a similar study, measuring impedance with respectto the fluid level of the animal. As with FIG. 19, the subjects shown inFIG. 20 experienced a drop in impedance as fluid was given to thesubjects, and then experienced a rise in impedance as fluid wassubsequently removed. The charts in FIG. 20 show the actual impedancevalues obtained in ohms with the Reveal LINQ system. In each of thecharts in FIG. 6, 2 L of fluid was infused into the subject over thecourse of approximately 20 minutes. This corresponds with the initialdownward slope of the impedance graphs. The time to remove the 2 L offluid depends on the rate of fluid removal from the dialysis machine. InFIG. 20 c, this rate was 1000 mL/hour removed. For all of the othergraphs in FIG. 20, the fluid removal rate was 750 mL/hour. The rate offluid removal did not alter the fact that the impedance values obtainedincreased with decreasing fluid of the subject. This can be seen bycomparing FIG. 20 c, with a fluid removal rate of 1000 mL/hour with theother figures showing fluid removal rates of 750 mL/hour. Likewise, thestarting fluid level of the subject did not change the fact that theimpedance varied with the fluid level. For example, in FIG. 20 d, thestarting impedance value with the LINQ system was about 1220 ohms. Theeffect on impedance due to fluid level for the subject of FIG. 20 d wassimilar to that shown in FIG. 20 b, where the starting impedance valuewas about 890 ohms.

The peak and trough of the charts in FIGS. 19-20 are important to healthcare providers. These points show the maximum and minimum fluid levelsachieved by the subject during the dialysis session. Failure to reachthe optimum fluid level for a patient during dialysis may show that thepatient is in a non-optimal, or a wet state after dialysis. If the fluidlevel drops below the optimum level for the patient, there may be anincreased risk of arrhythmias, hypotension, HF or other negativesymptoms.

FIG. 21 shows a sample output showing the relative fluid level of apatient between dialysis sessions. The dark bars at the start and end ofthe chart show the occurrence of a dialysis session. As can be seen, therelative fluid level of the patient increased in the time period betweenthe sessions. An output such as shown in FIG. 21 allows the health careproviders to see the rate and amount of increase in fluid level betweendialysis sessions. This, in turn, can allow the health care providers tochange the dialysis schedule for the patient, counsel the patient onmanaging their fluid intake or to change the fluid removal prescription.The data in FIG. 21 shows the fluid level of the patient at the end ofthe previous dialysis session. From this data, the health care providercan determine if the patient arrived at the target weight after theprevious dialysis session. The data in FIG. 21 also shows how quicklythe fluid level in the patient increased between sessions, and the fluidstatus of the patient before beginning the current dialysis session. Ifthe patient shows a higher than normal fluid level, or if the patientdid not reach the dry weight after the previous dialysis session, therate of fluid removal can be increased to ensure that the dry weight isreached during the current session. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, thiscan be done automatically by a computer in communication with thedialysis machine. In a typical dialysis session, about 2 L of fluid maybe removed from a patient. Some larger or non-compliant patients canhave more fluid removed, including 6-7 L or more. The output shown inFIG. 21 will enable the physician to determine how much fluid wasremoved in the previous dialysis session, and to set the target fluidremoval amounts and rates accordingly. The rate of fluid increasebetween dialysis sessions may be due to other factors, such as diet. Theability to see this information clearly can enable the health careprovider to identify causes of fluid accumulation and educate thepatient on a healthier lifestyle. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, longertime periods shown in the output display are contemplated. For example,the output display can show the entire week, including if there is along interdialytic window.

FIG. 22 shows the relative fluid level of a patient over a period of 13days. The dark bars on the chart show the occurrence of dialysis. Theestimated dry weight of the patient is also shown on the graph as “EDW”,and taken by the fluid level achieved by the patient during dialysis.Outputs such as shown in FIG. 22 allow the health care providers todetermine long-term trends for medical parameters such as the relativefluid level of the patient. The effect of each dialysis session can bequickly noted on the chart because the two variables are presentedtogether. Long term data concerning the fluid level of the patient withrespect to dialysis can be used by the health care providers in order todetermine whether the patient is approaching the target fluid levelafter each dialysis session. The rate at which fluid level increasesbetween sessions can be used for determining the optimal schedule fordialysis. For example, if the fluid level of the patient rises quicklybetween dialysis sessions, the rapid fluid level rise may signal a needto increase dialysis frequency. If the patient is having issues withlarge fluid gains between dialysis sessions, the large fluid gains maymean that the target fluid levels need to be changed. One skilled in theart will understand that a time period for the chart can be longer orshorter than the 13 days shown in FIG. 22. A long time period shown onthe chart can help the health care providers assess whether enough fluidis being removed each dialysis session, if there are any recurringproblems with large gains in fluid, or whether the target fluid level ofthe patient should be altered.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the systems described herein can use the datashown in FIG. 22, or in displays with longer time windows to determinethe average fluid level achieved after dialysis over the particular timewindow. This time window can be any window of interest. In anyembodiment of the second, third, and fourth inventions, the time windowchosen may be two dialysis sessions. In any embodiment of the second,third, and fourth inventions, the time window can be between any of twodialysis sessions to 1 year or longer, one week to two weeks, one weekto one month, two weeks to two months, one month to six months, or sixmonths to a year. After determining the average fluid level achievedover the time period of interest, the user can determine how thisparticular dialysis session compares with respect to the averagedialysis session over the time period. The user can also determine howclose to the target weight the patient has gotten over the time period,or warn the user if the patient is starting out in a wet state, or ifthey did not get to a completely dry state during the last dialysissession. The displays, such as shown in FIG. 22, show a long termdialysis efficiency index.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the frequency of measurements of other medicalparameters can be adjusted based on the patient fluid level relative tothe patient's estimated dry weight. For example, if the patient fluidlevel is far above the estimated dry weight, the frequency of monitoringother medical parameters can be increased during this time period.

One skilled in the art will understand that other medical parameters canbe included in the output of the first, second, third, fourth, fifth andsixth aspects of the invention. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the medicalparameters can include arrhythmia information, heart rate, fluid level,blood pressure, or potassium or other blood ion levels. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, multiple parameters can be included on a single chart.By way of example, a single chart can include both fluid level and theincidence of arrhythmia along with the occurrence of dialysis sessions.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the dialysis parameters shown on the outputcan include more than the time that dialysis took place. The amount offluid removed, the ultrafiltration rate, or the concentration of solutesin the dialysate can be included on the same chart.

One skilled in the art will understand that continuous monitoring ofevery medical parameter is not necessary. Certain medical parameters,such as dialysis symptoms, potassium level, calcium level, or blood pH,can be monitored periodically. Dialysis symptoms can include whether thepatient experienced pain, cramping, light headedness, and the severityof the symptoms. The output provided by the system can still show theoccurrence of dialysis and the periodically monitored medical parameter.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, parameters that are measured to be outside ofa predetermined range can be highlighted, or shown with a specificsymbol.

One skilled in the art will also understand that the particular timeperiod provided in the output of the first, second, third, fourth, fifthand sixth aspects of the invention is flexible. The time period can beselected based on the needs of the researchers or health care providers.For example, at the start of a dialysis session, understanding how thehealth status of the patient changed from the start to the end of theprevious dialysis session, along with how the patient respondedphysiologically to the dialysis, can be important. Fluid levels andarrhythmia information from the previous week can be valuable to thedialysis clinic for determining the effectiveness of the particulardialysis sessions. A longer period of time, such as week or months, maybe valuable in order to determine long term trends. If the user needs tosee the effects of dialysis on a particular medical parameter over along period of time, the output can be shown for weeks, months or years.The output can be shown for lengths of time between 1 hour and 1 year,1-2 hours, 1 hour-1 day, 4 hours-7 days, 1 day-1 month, 7 days-30 days,30 days-6 months, 4 months-1 year, or for longer than one year. Trendingthe information enables physicians to see patterns between dialysissession parameters and patient health episodes. By placing sequentialdialysis sequences next to one another, the physician can determine theeffectiveness of interventions in preventing futures symptoms.Typically, nephrologists will conduct a comprehensive review of thepatient each month. The ability to look at data concerning fluid levels,arrhythmias and blood pressure across 1, 2 or 4 weeks can enable thenephrologists to determine long term trends and if necessary change thedialysis prescription, anti-arrhythmic medications, dialysis frequencyor any other corrective action. If a shorter time period is needed, theoutput can be selected for hours or days. In any embodiment of thefirst, second, third, fourth, fifth and sixth aspects of the invention,the output can show one dialysis session, or a small portion, such asone hour or less, of a dialysis session. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, theuser can select the time period to be displayed. In any embodiment ofthe first, second, third, fourth, fifth and sixth aspects of theinvention, the display can be generated for months or years, but allowthe user to select the particular window of interest. For example, if anoutput display shows the incidence of arrhythmia corresponding todialysis sessions for an entire year, the user can select only theprevious week. The data for the previous week can then be shown larger,and in greater detail. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, the user canpre-select the time intervals for data to be shown. The user can enterin the specific hours, days, weeks, months, or years to be displayed. Inany embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, a sliding bar or other interface can be usedfor the user to be able to set the interval to be displayed.

Other dialysis information in addition to the occurrence of a dialysissession can be included in the output of the second, third, and fourthfirst, second, third, fourth, fifth and sixth aspects of the invention.Dialysis parameters such as the time a dialysis session takes place, thelength of a dialysis session, the amount of fluid removed, symptomsexperienced by the patient, and the dialysis session prescription canalso be included. These parameters can be included in the outputsdescribed herein. Providing specific parameters from each dialysissession in the output will allow the health care providers to determinethe effects of each dialysis parameter, and the effects of changing thedialysis parameters, on the monitored medical parameters.

The exact appearance of the output can be changed without departing fromthe scope of the first, second, third, fourth, fifth and sixth aspectsof the invention. Although many of the charts shown in FIGS. 15-22include the dialysis sessions as darkened bars, the dialysis sessionscan instead be shown as any suitable character or symbol. Further, theoccurrence of a dialysis session need not be shown on a single displayor chart with the monitored medical parameter. Instead, a second displaychart showing the occurrence of dialysis sessions can be included,wherein the two displays or charts are synchronized in time. Forinstance a chart showing when dialysis occurs along with the fluidremoved during dialysis could be shown in one panel, with arrhythmiainformation, blood pressure, and heart rate each shown in separatepanels below the dialysis chart. The chart can be created on a computermonitor, handheld device, programmer, dialysis machine display, orformatted for a paper printout.

The output displays of the first, second, third, fourth, fifth and sixthaspects of the invention can enable physicians or other users to see thetemporal link between the dialysis and observed arrhythmias or othermedical parameters. This can enables the physician to take actions toreduce the occurrence of negative dialysis effects.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, an output can be generated showing a medicalparameter with regards to dialysis treatment over the course of weeks,months, or years. The data can be compiled to show the average effectsof dialysis on a medical parameter. An example, showing the percentageover dry weight for a patient with respect to dialysis sessions is shownin FIG. 23. In FIG. 23 the percent over dry weight is shown in 12 hourperiods, with dialysis occurring at periods 1, 5, and 9. As is shown inFIG. 23, the % over dry weight for the patient was generally at or near0% immediately following dialysis, shown in period numbers 1, 5, and 9.After each dialysis session, the percent over dry weight increases,reaching a maximum of between 2 and 4 percent just prior to the nextdialysis session during the short interdialytic windows. The percentover dry weight increased to above 4% after the longer dialytic window,as shown in period number 14. The data presented in FIG. 23 shows thehealth care providers how effectively the dialysis works to keep thepatient near the dry weight over the course of a longer period of time.Heart rate variability is significantly linked to the patient's percentover dry weight. Higher excess fluid levels are associated withreductions in heart rate variability.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the data presented can be used to generaterisk factors for a patient with respect to the timing of dialysis, asshown in FIG. 24. FIG. 24 shows a negative binomial regression model ofthe relative risk of any review-confirmed arrhythmic episodes during thedialytic week computed across many patients. A similar display could becomputed for a single patient once data has been collected for anextended period of time. Each block refers to a twelve hour period withrespect to the previous dialysis session. The dependent variable of thenegative binomial regression is the number of arrhythmic events thesubject experienced during the each period. Clinically significantarrhythmias were defined as bradycardia of ≦40 bpm for ≧6 seconds,asystole of ≦3 seconds, sustained ventricular tachycardia of ≧130 bpmfor ≧30 seconds, and symptomatic arrhythmias. The analysis includes all12 hour periods, regardless of whether the period contained anyarrhythmic events. The estimated model includes a fixed event for eachof the 14 time periods shown in FIG. 24. Estimates of these period riskswere used to calculate the relative risks shown in the figure. The modelalso includes a full set of patient-specific fixed effects that (1)improve the estimated time period risk effects by controlling forvariation across patients in fixed characteristics (age, sec,comorbidity conditions, etc) and (2) allow for the dependence acrossrepeated observations on the same subject. This is critical in obtaininguseful standard error and confidence interval estimates for theestimated relative risks.

The white error bars within each of the bars of FIG. 24 indicate thelower bound of a one-sided 95% confidence interval for each relativerisk estimate. The interpretation of these estimates is that there is a95% confidence that the true relative risk for the time interval liesabove the lower edge of the white error bars. For periods where thelower edge of the white bar is above the reference, shown by the dashedline, there is a statistically significant (p<0.05) risk of arrhythmiain that period relative to the reference period, which is set as theperiod starting 12 hours after the first dialysis session of thedialytic week. As is shown in FIG. 24, for most periods with astatistically significant elevated risk, the lower bound of the errorbar is significantly above the reference, and that the p-values in thistime period would therefore be significantly smaller than the 0.05level.

As is noted in FIG. 24, the mean arrhythmic event rate for the referenceinterval is 0.244 events per 12 hours. Multiplying this factor by therelative risk for a time interval provides an estimate of the arrhythmicevent rate in that interval.

As can be seen in FIG. 24, the arrhythmic risk is concentrated in the 24hours centered on the start of each dialysis session. In particular,arrhythmic risk peaked during the 12 hour intervals beginning with eachdialysis session. The risk in these time periods is about three times asgreat as during the reference period. The arrhythmic risk falls sharplyafter the first 12 hours of the interdialytic window, before risingagain towards the end of the window. These results are consistent withthe findings of Foley, et al, N Engl J Med. 2011; 365(12):1099-1107, onvariation in the risk of CVD related hospitalization and death over thedialytic week in USRDS hemodialysis patients. The results shown in FIG.24 are also consistent with the results of Bleyer, et al, Kidneyinternational. June 2006; 69:(12)2268-2273 and Genovesi et al,Nephrology, Dialysis, Transplantation 2009; 24(8):2529-2536, onvariation in the risk of sudden cardiac death in ESRD patients with timeelapsed since the last hemodialysis session, and show that arrhythmiasare frequent during dialysis and strongly associated with the dialysisschedule.

One skilled in the art will understand that the choice of 12-hourperiods in the graph shown in FIG. 24 can be longer or shorter in any ofthe first, second, third, fourth, fifth and sixth aspects of theinvention. The risk periods used in the current invention are flexible.Further, the choice of the first 12-24 hour interval as the referencecan be changed. This period was chosen because the period is near thenadir of the risk for the entire week, but other periods can be chosenas the reference.

The relative risks shown in FIG. 24 allow the health care providers tosee if there is a need to change dialysis parameters, such as if therelative risk is highest immediately after dialysis, or schedule extradialysis sessions, such as if the relative risk is highest immediatelybefore dialysis sessions.

FIG. 25 shows one embodiment of a system that can be used for medicalmonitoring in accordance with the first, second, third, fourth, fifthand sixth aspects of the invention. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, anImplantable Medical Device (IMD) 1601 can be implanted in a patient1602. The IMD can measure impedance and/or ECG measurements. The IMD canbe a pacemaker or implantable cardioverterdefibrillator (ICD). Inembodiments where sensing is external the patient, a wireless electrodepatch and system for measuring the physiological condition of a subjectcan be used. The electrode patch for ECG monitoring can include relatedmethods for sensing, analyzing and/or transmitting or relaying aphysiological signal to a processor or computer configured to receivethe data. The wireless electrode patch and system can be lightweight,compact and reusable. The wireless electrode patch can also provide alow, power system for extended battery life and use. The wirelesselectrode can be applied as a single patch but can be configured as morethan one patch.

The IMD 1601 can comprise one or more sensors that can monitor at leastone medical parameter as explained herein. The IMD 1601 can beconfigured to receive a signal alerting the device that a dialysissession has begun, or that a dialysis session is ongoing, such as signal1604 sent from transmitter 1605 on dialysis machine 1606. A processor,as may be included in computer 1603 in electronic communication with theIMD 1601 through signal 1607 can obtain the medical data from the IMD1601 and display an output showing both the dialysis sessions for thepatient and the monitored medical parameter for the same time period. Inany embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the IMD 1601 can be configured to transmit themedical information to an electronic health record or other system usingsignal 1607. The processor can collect the data from the IMD and thedialysis data from a dialysis machine or by manual entry, andsynchronize the data while recording the time of the dialysis session.In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the data collected and synchronized by theprocessor in computer 1603 can transmit the data to the patient'smedical records or any other receiver. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, theprocessor can be a part of the dialysis machine 1606, the IMD 1601 orany other component, and need not be inside of an external computer. Inany embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the IMD 1601 can be configured with a receiverto receive data on the dialysis session or from the patient's electronicmedical records. This enables the processor to be configured as part ofthe IMD 1601. The IMD 1601 can then consolidate the data and send thedata to a receiver, programmer, computer, handheld system, or any othersystem known in the art with the data in consolidated form.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, more than one set of data can be provided on asingle chart. The processor can synchronize multiple sets of data, suchas arrhythmia information, blood solute levels, blood pressure, orothers with the dialysis information. This can be sent to the patient'sEMD or a handheld device as described herein and provided as one or morecharts showing all the information of interest to the user.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the processor can receive the dialysisinformation directly from a source other than the IMD. For example, auser can manually input the dialysis information into a computer or apatient could enter symptom data into a handheld which synchronizes withthe EMR or IMD. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the dialysis machine cantransmit the dialysis information to the processor through either wiredor wireless means.

The IMD 1601 can send and receive data through any standardcommunication protocol, including Bluetooth Low Energy, radio frequency,cellular or any other type of communication known in the art. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the IMD 1601 can be configured so that the IMD 1601begins to send and receive data as soon as the IMD 1601 is proximity tothe dialysis machine 1606 or processor 1603. The IMD 1601 can beconfigured to detect a magnetic field or triggering radio frequencysignal sent by the dialysis machine 1606 or processor 1603. This wouldenable the IMD to refrain from transmitting information when the IMD1601 is not within range of a receiver. In any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention, the IMD1601 can be configured to automatically record the start of a dialysissession in response to the trigger signal detected from dialysis machine1606. In any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, the communication between the dialysismachine, IMD and processor can be encrypted to protect patient privacy.

FIG. 26 shows a chart with possible signal transmissions from each ofthe components of a medical monitoring system. External medical devices1701, such as EKGs, blood pressure cuffs, hematocrit measurementmachines, or other medical devices, can be configured to transmitpatient health data to the central processor 1706. An implantablemedical device 1702 can transmit data such as impedance, heart rate orarrhythmia information to the processor 1706. Other information, such ashow the patient feels can be transmitted by manual entry through userinterface 1703. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the dialysis information canalso be transmitted to the processor by manual entry through userinterface 1703. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, the dialysis information canbe directly obtained from a dialysis machine 1704. In any embodiment ofthe first, second, third, fourth, fifth and sixth aspects of theinvention, the timing and prescription of a dialysis session can beobtained from the patient's electronic medical records 1705.

The processor 1706 shown in FIG. 26 can in any embodiment of the first,second, third, fourth, fifth and sixth aspects of the invention can be apart of any of the devices described. The processor 1706 can be integralto the IMD, any external diagnostic device or the dialysis machine. Inany embodiment of the first, second, third, fourth, fifth and sixthaspects of the inventions the processor can be in an external computer,such as is shown in FIG. 25.

The processor 1706 of FIG. 26 can be configured to synchronize theinputs received from each of the sources. In this way, the processor1706 can create an output showing the medical and dialysis parameters ofinterest simultaneously.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the processor 1706 can be configured tocommunicate with a network hub 1707, such as the CareLink Network Hub.The network hub 1707 can organize and store the data from the processor1706 and prepare and transmit the data in the desired output form. Inany embodiment, the network hub 1707 can transmit the data to a handhelddevice 1708, a web browser based application 1709 or to a programmer1710.

Not all of the sources shown in FIG. 26 are necessary for the first,second, or third inventions. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, any of thesources can be removed. For example, the dialysis information can beobtained from a dialysis machine 1704, and the system need not beconfigured to obtain this information from the electronic medicalrecords 1705. Similarly, the user interface 1703 can be eliminated ifall parameters of interest can be monitored with sensors.

The network hub 1707 is not necessary for the first, second, or thirdinventions. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the processor 1706 can perform thefunctions described herein as being performed by the network hub 1707.The output can be transmitted to any device or application capable ofreceiving the data. Other possible embodiments of the first, second,third, fourth, fifth and sixth aspects of the invention for the outputreceiver are known in the art and are within the scope of the first,second, third, fourth, fifth and sixth aspects of the invention. In anyembodiment of the second, third, and fourth inventions, the output canbe sent directly to a receiver on the dialysis machine. The dialysismachine can automatically suggest or implement changes to the dialysissession based on the received data, as explained herein.

One skilled in the art will understand that any IMD capable of sensingand transmitting data concerning patient medical parameters is withinthe scope of the first, second, third, fourth, fifth and sixth aspectsof the invention. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, medical parameters can besensed and transmitted by hemodialysis machines, drug delivery devices,ILRs, blood panels, or the system may use a micro-fluidics basedambulatory blood composition monitor, which would allow physicians tosee the post-dialysis session physiology (and normalizationcurves/oscillations) as the body seeks to rebalance post intervention.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the dialysis information can automatically betransmitted to the IMD. A dialysis machine can include a transmitter,and the IMD can include a receiver. The transmitter can transmit atrigger signal, which can automatically be received by the IMD. Uponreceipt of the trigger signal, the IMD can automatically record theoccurrence and duration of the dialysis session. Non-limiting examplesof technology capable of transmitting the trigger signal include radiofrequency transmission, Bluetooth, cellular transmission, magneticfields, and mechanical vibration (e.g. tapping of the device).

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention the trigger signal can be caused by proximityto the dialysis machine. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, when the patient iswithin a certain distance of the dialysis machine, the system can assumethat the proximity is because the patient is receiving dialysis. In anyembodiment of the first, second, third, fourth, fifth and sixth aspectsof the invention, the trigger signal can be sent by the dialysis machinewhen a dialysis session begins and ends. The transmitter need not be onthe dialysis machine itself, and can instead be located away from thedialysis machine, but in electronic communication with the dialysismachine. Any system wherein the transmitter can determine if dialysis isoccurring is within the scope of the first, second, third, fourth, fifthand sixth aspects of the invention.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the proximity of the patient to the processorcan automatically cause the transmission of the medical data to theprocessor. The transmission of dialysis data to the processor can becaused by the same trigger, or the information can manually be enteredby the user.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, a user interface can be included in the systemto allow the entry of patient reported medical parameters and symptoms.For example, the user can input whether the patient experienced pain orcramping in the period since the last dialysis session. The user canalso input the severity of any symptoms experienced by the patient. Inany embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the approximate timing and severity of thesymptoms can be entered, such as shortly after the last dialysissession, or shortly before the current dialysis session. The outputshown can include these non-measured symptoms along with the symptomsmeasured by sensors, which can allow the health care providers to adjustdialysis parameters in order to maximize the patient's quality of life.

The medical monitoring of the first, second, third, fourth, fifth andsixth aspects of the invention can be adapted to determine the propercourse of action in response to medical parameters of the patient. FIG.27 shows the traditional approach to treating dialysis patientsexperiencing arrhythmia or atrial fibrillation. If an atrialfibrillation event is observed in a patient at step 1801, the patient ordata can be referred to a cardiologist 1802. In response, thecardiologist can recommend ablation, anticoagulation medication, atrialappendage occlusion, or change the rate of ultrafiltration 1803.However, because there is no reliable method to determine if the atrialfibrillation event is due to dialysis factors, the correct course ofaction is difficult to determine.

FIG. 28 shows how the first, second, third, fourth, fifth and sixthaspects of the invention can result in better accuracy in determiningthe correct course of action. If the system detects an atrialfibrillation or arrhythmia event in step 1901, the system can determineif the event is a new onset event in step 1902. That is, because thesystem is monitoring and recording the atrial fibrillation or arrhythmiaevents, the system can automatically determine if there is an increasein the amount of time spent in atrial fibrillation or arrhythmia eventsas compared to a previous time window. If the system determines that thedetected atrial fibrillation or arrhythmia event is not a new onsetevent, the system can also determine whether there has been an increasein the amount of atrial fibrillation or arrhythmia during the previousweek at step 1903. If there has been an increase, the event can bereported to a cardiologist in step 1904, and the cardiologist can takesteps to reduce the occurrence of the events in step 1905. The step ofreporting or referring the events to a cardiologist can be accomplishedby any means known in the art. In any embodiment of the first, second,third, fourth, fifth and sixth aspects of the invention, the reportingor referring can be accomplished by automatically sending a report tothe patient's electronic medical records, to a medical data hub, or to ahandheld device.

The system of the first, second, third, fourth, fifth and sixth aspectsof the invention can also determine whether the atrial fibrillation orarrhythmia events are related to the occurrence of a dialysis session instep 1906. Whether the atrial fibrillation or arrhythmia event isrelated to the occurrence of a dialysis session can mean that thearrhythmia or atrial fibrillation is related temporally to theoccurrence of the dialysis session or mathematically to any dialysisparameter. The process to determine whether the events are linked todialysis is described herein. An increase in events before, during, orshortly after dialysis sessions could show that the events are tied tothe dialysis. In any embodiment of the first, second, third, fourth,fifth and sixth aspects of the invention, this determination can be madeautomatically by the medical monitoring system. One skilled in the artwill understand that algorithms can be programmed into the medicalmonitoring system to determine how the timing of the atrial fibrillationor arrhythmia events relate to the timing of dialysis.

If the atrial fibrillation or arrhythmia event is linked to dialysis,the system can also determine whether the event is linked to fluidmanagement in step 1907. Fluid management refers to the fluid levels ofthe patient, including the pre-dialysis or post-dialysis weight, thechange in fluid levels of the patient during a dialysis session orduring an interdialytic window, or the rate of fluid level change in thepatient. Using the data described above, the system can compare thepatient fluid level over a recent time period to the historical fluidlevel for the patient in step 1908. The system can also determine acorrect course of action to take in response to the patient fluid levelin step 1909. If the patient fluid level is higher than the historicalfluid level for the same patient, the system can lower the patient dryweight. If the patient's fluid level is less than the historical fluidlevel for the same patient, the system can increase the patient dryweight. The adjustments to patient dry weight can be made automaticallyby the medical monitoring system, or can be in the form of suggestionsto the health care professionals.

The system can also compare the rate and magnitude of the decrease inpatient fluid level during dialysis to the historical trend for thepatient in step 1910. A rate increase over previous dialysis sessions influid level of the patient may indicate that the new onset arrhythmia oratrial fibrillation events are due to the rate of fluid removal. Thedata from the previous dialysis sessions can be stored in a memorydevice. In any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, the memory device can be anon-transitory memory configured to receive and store the data. Anincrease in the magnitude of the fluid drop compared to the historicallevels for the patient may indicate that the new onset arrhythmia oratrial fibrillation events are due to the magnitude of ultrafiltration.In either case, the system can determine a course of action in step1911. If the ultrafiltration rate is the cause of the arrhythmia oratrial fibrillation, the system can reduce the ultrafiltration rate. Ifthe ultrafiltration magnitude is the cause of the arrhythmia or atrialfibrillation, the system can reduce the ultrafiltration magnitude. Thechanges to ultrafiltration described in step 1911 can be madeautomatically by the medical system, or can be in the form of suggestedcourses of action to the health care professionals in the form ofelectronic additions to the patient's electronic medical records, anelectronic-mediated communication to a handheld data receiver orhospital data hub, or the suggestions can be transmitted electronicallyto the health care professionals.

In addition to determining whether the new onset arrhythmia or atrialfibrillation event is due to fluid management in step 1907, the systemof the first, second, third, fourth, fifth and sixth aspects of theinvention can also determine if the events are due to electrolytemanagement in step 1912. Electrolyte management refers to theconcentration of any electrolyte in the patient's blood or in the fluidsused during dialysis. In any embodiment of the first, second, third,fourth, fifth and sixth aspects of the invention, the system candetermine whether the events are due to both electrolyte managementfluid management. As explained herein, the system can monitorelectrolyte or other blood solutes in the patient and determine theeffect of dialysis on the concentration of these solutes. If the systemdetermines that there is a significant change in potassium, calcium orother electrolyte levels in the patient due to dialysis, and that thesechanges correspond to the new onset arrhythmia or atrial fibrillationevents in the patient in step 1912, the system can determine possiblecourse of action in step 1913. For example, if the events are linked toincreases or decreases in patient potassium level, the system canincrease or decrease the concentration of potassium ions in thedialysate as indicated by the term “potassium bath,” which describes thedialysate potassium concentration. If the events are linked to increasesor decreases in calcium levels, the system can increase or decrease theamount of calcium added to the dialysate. The dialysate concentration ofany ion can be adjusted in response to data showing that the new onsetarrhythmia or atrial fibrillation events are due to electrolytemanagement. The changes described in step 1913 can be done automaticallyby the system, or can take the form of suggestions to the health careprofessionals in the form of electronic additions to the patient'selectronic medical records, an electronic-mediated communication to ahandheld data receiver, hospital data hub or medical server, or thesuggestions can be transmitted electronically to the health careprofessionals.

Often, arrhythmia or atrial fibrillation events can be due to bothelectrolyte and fluid management. As such, the system can determine bothof whether there are fluid management factors and whether there areelectrolyte management factors. If both electrolyte management and fluidmanagement factors are present, the system can take any of the actionsdescribed above, alone or in combination.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the invention, the system or user can take other actions inresponse to dialysis linked arrhythmia or atrial fibrillation events instep 1914. In any embodiment of the first, second, third, fourth, fifthand sixth aspects of the invention, the patient can be switched to homedialysis. If the events are due to a longer interdialytic window, asdescribed herein, the system can add an extra dialysis session. If thepatient is not reaching the estimated dry weight, the length of thedialysis session can be increased. This is especially true if the rateof ultrafiltration needs to be decreased. The system can also change theultrafiltration speed or magnitude. The system can optimize patientmedicines or electrolytes. The system can suggest alternativeanticoagulation medications. In any embodiment, the system can suggestthe use of medications to control heart rate, such as beta blockers. Thesystem can also suggest nutritional counseling or possibly moreaggressive arrhythmia management. Examples of drugs that can bedelivered or suggested for the patient include Amiodarone (Cordarone,Pacerone), Bepridil Hydrochloride (Vascor), Disopyramide (Norpace),Dofetilide (Tikosyn), Dronedarone (Multaq), Flecainide (Tambocor),Ibutilide (Corvert), Lidocaine (Xylocaine), Procainamide (Procan,Procanbid), Propafenone (Rythmol), Propranolol (Inderal), Quinidine,Sotalol (Betapace), or Tocainide (Tonocarid), or any other drug known inthe art. Any of these changes can be made automatically to the dialysissystem, be automatically imported electronically into the patient'selectronic medical records, an electronic-mediated communication to ahandheld data receiver, hospital data hub or medical server, or can beelectronically transmitted as suggestions to the health careprofessionals.

In any embodiment of the first, second, third, fourth, fifth and sixthaspects of the inventions, the system can also adjust monitoring inresponse to dialysis linked arrhythmias in step 1915. For example, thesystem can automatically suggest an increase in the rate of blood labwork. In any embodiment of the first, second, third, fourth, fifth andsixth aspects of the invention, the system can continue to monitor theatrial fibrillation or arrhythmia burden and timing with respect todialysis in order to determine the effectiveness of the interventionsdescribed.

One skilled in the art will understand that various combinations and/ormodifications and variations can be made in the dialysis systemdepending upon the specific needs for operation. Moreover, featuresillustrated or described as being part of an aspect of the invention canbe included in the aspect of the invention, either alone or incombination.

We claim:
 1. A device, comprising: one or more sensors in electroniccommunication with a medical device processor, the sensors obtaining oneor more physiological parameters of a patient and communicating the oneor more physiological parameters to a medical device processor; and themedical device processor using an algorithm to determine a probabilityof a ventricular arrhythmia based on the physiological parameters. 2.The device of claim 1, wherein the algorithm, for each of the one ormore physiological parameters of a patient, calculates an individualhazard estimate given by y₁(t)=h₁(t){circle around (×)}x₁(t), whereinh₁(t)=k₁e^(k) ² ^(t), t is time, y₁(t) is the individual hazard estimateat time t, k₁ and k₂ are constants for the given parameter, x₁(t) is thephysiological parameter at time t, and {circle around (×)} is aconvolution operator.
 3. The device of claim 2, wherein the convolutionoperator is defined by [h₁ {circle around (×)}x₁](n)=Σ_(m=−∞)^(+∞)h₁(m)x₁(n−m), wherein n is time, h₁(n)=k₁e^(k) ² ^(n), x₁(n) is thephysiological parameter at time n, and k₁ and k₂ are constants for thegiven parameter.
 4. The device of claim 3 wherein the algorithm furthercalculates a total hazard function: f(t)=Σ_(m=1) ^(n)y_(m)(t), whereinf(t) is the total hazard at time t, y_(m)(t) is an individual hazardestimate for parameter m at time t, and n is the number of physiologicalparameters used.
 5. The device of claim 3 wherein the algorithm furthercalculates a total hazard function:${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{y_{m}{(t)}}})}}} \right) + k_{9}}$wherein f(t) is the total hazard at time t, y_(m)(t) is an individualhazard estimate for parameter m at time t, k₉ is an offset coefficient,and n is the number of physiological parameters used.
 6. The device ofclaim 5, wherein the device is configured to allow for entering actualresults at time t for the patient into the medical device processor, andthe algorithm further calculates a total error as the difference betweenthe total hazard and the actual results at time t, and the algorithmfurther adjusts each of the coefficients to minimize the error.
 7. Thedevice of claim 1, wherein the physiological parameters are any one ofselected from the group consisting of tissue impedance, number ofspontaneous ventricular tachycardia and ventricular fibrillation eventsper day, time spent in atrial fibrillation, and dialysis status data. 8.A method for determining the probability of a life threateningventricular arrhythmia, comprising the steps of: obtaining one or morephysiological parameters of a patient from one or more sensors;communicating the physiological parameters to a medical deviceprocessor; wherein the medical device processor utilizes an algorithm todetermine a probability of a ventricular arrhythmia.
 9. The method ofclaim 8, wherein the physiological parameters are tissue impedance,number of spontaneous ventricular tachycardia and ventricularfibrillation events per day, time spent in atrial fibrillation, andinformation about the dialysis status data.
 10. The method of claim 8wherein at least one of the physiological parameters is obtained from asensor that is a component of an implantable medical device.
 11. Themethod of claim 8 wherein the medical device processor further comprisesa signaling mechanism to signal if the probability of ventriculararrhythmia is greater than a pre-set value.
 12. The method of claim 8,wherein the algorithm, for each of the one or more physiologicalparameters of a patient, calculates an individual hazard estimate givenby y₁(t)=h₁(t){circle around (×)}x₁(t), wherein h₁(t)=k₁e^(k) ² ^(t), tis time, y₁(t) is the individual hazard estimate at time t, k₁ and k₂are constants for the given parameter, x₁(t) is the physiologicalparameter at time t, and {circle around (×)} is a convolution operator.13. The method of claim 12, wherein the convolution operator is definedby [h₁ {circle around (×)}x₁](n)=Σ_(m=−∞) ^(+∞)h₁(m)x₁(n−m), wherein nis time, h₁(n)=k₁e^(k) ² ^(n), x₁(n) is the physiological parameter attime n, and k₁ and k₂ are constants for the given parameter.
 14. Themethod of claim 13 wherein the algorithm further calculates a totalhazard function: f(t)=Σ_(m=1) ^(n)y_(m)(t), wherein f(t) is the totalhazard at time t, y_(m)(t) is an individual hazard estimate forparameter m at time t, and n is the number of physiological parametersused.
 15. The method of claim 13 wherein the algorithm furthercalculates a total hazard function:${f(t)} = {\left( \frac{1}{1 + ^{- {({\sum\limits_{m = 1}^{n}{y_{m}{(t)}}})}}} \right) + k_{9}}$wherein f(t) is the total hazard at time t, y_(m)(t) is an individualhazard estimate for parameter m at time t, k₉ is an offset coefficient,and n is the number of physiological parameters used.
 16. The method ofclaim 15, further comprising the step of entering actual results at timet for the patient into the medical device processor, and the algorithmfurther calculates a total error as the difference between the totalhazard and the actual results at time t, and the algorithm furtheradjusts each of the coefficients to minimize the error.
 17. The methodof claim 16, wherein the algorithm further utilizes an adaptive filterto alter the coefficients for each patient periodically.
 18. The methodof claim 17 wherein the algorithm utilizes the adaptive filter to alterthe coefficients for each patient every 14 days.
 19. The method of claim9 wherein the information about the dialysis status data is enteredmanually.
 20. The method of claim 9 wherein the information about thedialysis status data is communicated to the medical device processorfrom an electronic medical record.
 21. The method of claim 15 furthercomprising the step of entering actual results at time t for a group ofpatients into the medical device processor, and the algorithm furthercalculates a total error as the difference between the total hazard andthe actual results for the group of patients at time t, and thealgorithm further adjusts each the coefficients for each patient tominimize the error.